llm: add solar pro (preview) (#6846)

llm/patches/0008-solar-pro.patch

@@ -0,0 +1,402 @@
+From 8313ce5f43f11f3d84f352f97f3802792e90e18c Mon Sep 17 00:00:00 2001
+From: Michael Yang <mxyng@pm.me>
+Date: Mon, 16 Sep 2024 15:53:16 -0700
+Subject: [PATCH] add solar-pro support
+
+solar-pro introduces block skip connections where blocks are connected
+to other, non-sequential blocks with a scale multiple
+
+this change adds 4 new keys to store the skip connections and one new
+tensor to store the scalar. the scalar is implemented a 1-dimensional
+tensor with 2 elements dervied from the model's bskcn_tv configuration.
+in general, the values are (bskcn_tv, 1 - bskcn_tv)
+---
+ src/llama.cpp | 267 +++++++++++++++++++++++++++++++++++++++++++++++---
+ 1 file changed, 254 insertions(+), 13 deletions(-)
+
+diff --git a/src/llama.cpp b/src/llama.cpp
+index f79bd782..b7771f53 100644
+--- a/src/llama.cpp
++++ b/src/llama.cpp
+@@ -213,6 +213,7 @@ enum llm_arch {
+     LLM_ARCH_NEMOTRON,
+     LLM_ARCH_EXAONE,
+     LLM_ARCH_RWKV6,
++    LLM_ARCH_SOLAR,
+     LLM_ARCH_UNKNOWN,
+ };
+ 
+@@ -261,6 +262,7 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
+     { LLM_ARCH_NEMOTRON,        "nemotron"     },
+     { LLM_ARCH_EXAONE,          "exaone"       },
+     { LLM_ARCH_RWKV6,           "rwkv6"        },
++    { LLM_ARCH_SOLAR,           "solar"        },
+     { LLM_ARCH_UNKNOWN,         "(unknown)"    },
+ };
+ 
+@@ -314,6 +316,7 @@ enum llm_kv {
+     LLM_KV_ATTENTION_KV_LORA_RANK,
+     LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT,
+     LLM_KV_ATTENTION_SLIDING_WINDOW,
++    LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION,
+ 
+     LLM_KV_ROPE_DIMENSION_COUNT,
+     LLM_KV_ROPE_FREQ_BASE,
+@@ -405,19 +408,20 @@ static const std::map<llm_kv, const char *> LLM_KV_NAMES = {
+     { LLM_KV_TIME_MIX_EXTRA_DIM,                "%s.time_mix_extra_dim"                },
+     { LLM_KV_TIME_DECAY_EXTRA_DIM,              "%s.time_decay_extra_dim"              },
+ 
+-    { LLM_KV_ATTENTION_HEAD_COUNT,             "%s.attention.head_count"             },
+-    { LLM_KV_ATTENTION_HEAD_COUNT_KV,          "%s.attention.head_count_kv"          },
+-    { LLM_KV_ATTENTION_MAX_ALIBI_BIAS,         "%s.attention.max_alibi_bias"         },
+-    { LLM_KV_ATTENTION_CLAMP_KQV,              "%s.attention.clamp_kqv"              },
+-    { LLM_KV_ATTENTION_KEY_LENGTH,             "%s.attention.key_length"             },
+-    { LLM_KV_ATTENTION_VALUE_LENGTH,           "%s.attention.value_length"           },
+-    { LLM_KV_ATTENTION_LAYERNORM_EPS,          "%s.attention.layer_norm_epsilon"     },
+-    { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,      "%s.attention.layer_norm_rms_epsilon" },
+-    { LLM_KV_ATTENTION_CAUSAL,                 "%s.attention.causal"                 },
+-    { LLM_KV_ATTENTION_Q_LORA_RANK,            "%s.attention.q_lora_rank"            },
+-    { LLM_KV_ATTENTION_KV_LORA_RANK,           "%s.attention.kv_lora_rank"           },
+-    { LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count" },
+-    { LLM_KV_ATTENTION_SLIDING_WINDOW,         "%s.attention.sliding_window"         },
++    { LLM_KV_ATTENTION_HEAD_COUNT,             "%s.attention.head_count"               },
++    { LLM_KV_ATTENTION_HEAD_COUNT_KV,          "%s.attention.head_count_kv"            },
++    { LLM_KV_ATTENTION_MAX_ALIBI_BIAS,         "%s.attention.max_alibi_bias"           },
++    { LLM_KV_ATTENTION_CLAMP_KQV,              "%s.attention.clamp_kqv"                },
++    { LLM_KV_ATTENTION_KEY_LENGTH,             "%s.attention.key_length"               },
++    { LLM_KV_ATTENTION_VALUE_LENGTH,           "%s.attention.value_length"             },
++    { LLM_KV_ATTENTION_LAYERNORM_EPS,          "%s.attention.layer_norm_epsilon"       },
++    { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS,      "%s.attention.layer_norm_rms_epsilon"   },
++    { LLM_KV_ATTENTION_CAUSAL,                 "%s.attention.causal"                   },
++    { LLM_KV_ATTENTION_Q_LORA_RANK,            "%s.attention.q_lora_rank"              },
++    { LLM_KV_ATTENTION_KV_LORA_RANK,           "%s.attention.kv_lora_rank"             },
++    { LLM_KV_ATTENTION_RELATIVE_BUCKETS_COUNT, "%s.attention.relative_buckets_count"   },
++    { LLM_KV_ATTENTION_SLIDING_WINDOW,         "%s.attention.sliding_window"           },
++    { LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION,  "%s.attention.block_skip_connection.%d" },
+ 
+     { LLM_KV_ROPE_DIMENSION_COUNT,          "%s.rope.dimension_count"                 },
+     { LLM_KV_ROPE_FREQ_BASE,                "%s.rope.freq_base"                       },
+@@ -589,6 +593,7 @@ enum llm_tensor {
+     LLM_TENSOR_ENC_FFN_DOWN,
+     LLM_TENSOR_ENC_FFN_UP,
+     LLM_TENSOR_ENC_OUTPUT_NORM,
++    LLM_TENSOR_BSKCN_TV,
+ };
+ 
+ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NAMES = {
+@@ -1408,6 +1413,24 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
+             { LLM_TENSOR_CHANNEL_MIX_RECEPTANCE,    "blk.%d.channel_mix_receptance" },
+         },
+     },
++    {
++        LLM_ARCH_SOLAR,
++        {
++            { LLM_TENSOR_TOKEN_EMBD,      "token_embd" },
++            { LLM_TENSOR_OUTPUT_NORM,     "output_norm" },
++            { LLM_TENSOR_OUTPUT,          "output" },
++            { LLM_TENSOR_ATTN_NORM,       "blk.%d.attn_norm" },
++            { LLM_TENSOR_ATTN_Q,          "blk.%d.attn_q" },
++            { LLM_TENSOR_ATTN_K,          "blk.%d.attn_k" },
++            { LLM_TENSOR_ATTN_V,          "blk.%d.attn_v" },
++            { LLM_TENSOR_ATTN_OUT,        "blk.%d.attn_output" },
++            { LLM_TENSOR_FFN_NORM,        "blk.%d.ffn_norm" },
++            { LLM_TENSOR_FFN_GATE,        "blk.%d.ffn_gate" },
++            { LLM_TENSOR_FFN_DOWN,        "blk.%d.ffn_down" },
++            { LLM_TENSOR_FFN_UP,          "blk.%d.ffn_up" },
++            { LLM_TENSOR_BSKCN_TV,        "bskcn_tv" },
++        },
++    },
+     {
+         LLM_ARCH_UNKNOWN,
+         {
+@@ -2237,6 +2260,7 @@ enum e_model {
+     MODEL_15B,
+     MODEL_16B,
+     MODEL_20B,
++    MODEL_22B,
+     MODEL_30B,
+     MODEL_34B,
+     MODEL_35B,
+@@ -2284,6 +2308,8 @@ struct llama_hparams {
+     std::array<uint32_t, LLAMA_MAX_LAYERS> n_head_kv_arr;
+     std::array<uint32_t, LLAMA_MAX_LAYERS> n_ff_arr;
+ 
++    std::array<std::array<uint32_t, LLAMA_MAX_LAYERS>, 4> n_bskcn_arr;
++
+     uint32_t n_layer_dense_lead = 0;
+     uint32_t n_lora_q = 0;
+     uint32_t n_lora_kv = 0;
+@@ -2349,6 +2375,7 @@ struct llama_hparams {
+         if (this->n_head_arr    != other.n_head_arr)    return true;
+         if (this->n_head_kv_arr != other.n_head_kv_arr) return true;
+         if (this->n_ff_arr      != other.n_ff_arr)      return true;
++        if (this->n_bskcn_arr   != other.n_bskcn_arr)   return true;
+ 
+         if (this->n_rel_attn_bkts    != other.n_rel_attn_bkts)    return true;
+         if (this->n_layer_dense_lead != other.n_layer_dense_lead) return true;
+@@ -2455,6 +2482,14 @@ struct llama_hparams {
+             return ssm_d_state * ssm_d_inner;
+         }
+     }
++
++    bool n_bskcn(uint32_t n, uint32_t il = 0) const {
++        if (il < n_layer) {
++            return n_bskcn_arr[n][il] > 0;
++        }
++
++        GGML_ABORT("fatal error");
++    }
+ };
+ 
+ static_assert(std::is_trivially_copyable<llama_hparams>::value, "llama_hparams must be trivially copyable");
+@@ -2635,6 +2670,8 @@ struct llama_layer {
+     struct ggml_tensor * ffn_gate_scale;
+     struct ggml_tensor * ffn_up_scale;
+     struct ggml_tensor * ffn_down_scale;
++
++    struct ggml_tensor * bskcn_tv;
+ };
+ 
+ // very similar to llama_batch,
+@@ -5937,6 +5974,21 @@ static void llm_load_hparams(
+                     default: model.type = e_model::MODEL_UNKNOWN;
+                 }
+             } break;
++        case LLM_ARCH_SOLAR:
++            {
++                ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
++
++                for (int i = 0; i < hparams.n_bskcn_arr.max_size(); ++i) {
++                    auto & bskcn = hparams.n_bskcn_arr.at(i);
++                    bskcn.fill(0);
++                    ml.get_key_or_arr(::format(LLM_KV_NAMES.at(LLM_KV_ATTENTION_BLOCK_SKIP_CONNECTION), LLM_ARCH_NAMES.at(ml.llm_kv.arch), i), bskcn, hparams.n_layer, false);
++                }
++
++                switch (hparams.n_layer) {
++                    case 64: model.type = e_model::MODEL_22B; break;
++                    default: model.type = e_model::MODEL_UNKNOWN;
++                }
++            }
+         default: (void)0;
+     }
+ 
+@@ -8420,6 +8472,38 @@ static bool llm_load_tensors(
+                     }
+ 
+                 } break;
++            case LLM_ARCH_SOLAR:
++                {
++                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
++
++                    // output
++                    {
++                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
++                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
++                    }
++
++                    for (int i = 0; i < n_layer; ++i) {
++                        ggml_context * ctx_layer = ctx_for_layer(i);
++                        ggml_context * ctx_split = ctx_for_layer_split(i);
++
++                        auto & layer = model.layers[i];
++
++                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
++
++                        layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_head_k * n_head});
++                        layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K,   "weight", i), {n_embd, n_embd_k_gqa});
++                        layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V,   "weight", i), {n_embd, n_embd_v_gqa});
++                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd});
++
++                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
++
++                        layer.bskcn_tv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_BSKCN_TV, "weight"), {2}, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0));
++
++                        layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
++                        layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
++                        layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
++                    }
++                } break;
+             default:
+                 throw std::runtime_error("unknown architecture");
+         }
+@@ -15173,6 +15257,158 @@ struct llm_build_context {
+ 
+         return gf;
+     }
++
++    ggml_cgraph * build_solar() {
++        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, llama_model_max_nodes(model), false);
++
++        // mutable variable, needed during the last layer of the computation to skip unused tokens
++        int32_t n_tokens = this->n_tokens;
++
++        const int64_t n_embd_head = hparams.n_embd_head_v;
++        GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
++        GGML_ASSERT(n_embd_head == hparams.n_rot);
++
++        struct ggml_tensor * cur;
++        struct ggml_tensor * inpL;
++
++        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
++
++        // inp_pos - contains the positions
++        struct ggml_tensor * inp_pos = build_inp_pos();
++
++        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
++        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
++
++        struct ggml_tensor * bskcn_1;
++        struct ggml_tensor * bskcn_2;
++
++        for (int il = 0; il < n_layer; ++il) {
++            struct ggml_tensor * inpSA = inpL;
++
++            if (hparams.n_bskcn(0, il)) {
++                bskcn_1 = inpSA;
++            }
++
++            if (hparams.n_bskcn(1, il)) {
++                bskcn_2 = inpSA;
++            }
++
++            if (hparams.n_bskcn(2, il)) {
++                inpSA = ggml_add(
++                   ctx0,
++                   ggml_mul(ctx0, bskcn_1, ggml_view_1d(ctx0, model.layers[il].bskcn_tv, 1, 0)),
++                   ggml_mul(ctx0, inpSA, ggml_view_1d(ctx0, model.layers[il].bskcn_tv, 1, ggml_element_size(model.layers[il].bskcn_tv))));
++            }
++
++            if (hparams.n_bskcn(3, il)) {
++                inpSA = ggml_add(
++                   ctx0,
++                   ggml_mul(ctx0, bskcn_2, ggml_view_1d(ctx0, model.layers[il].bskcn_tv, 1, 0)),
++                   ggml_mul(ctx0, inpSA, ggml_view_1d(ctx0, model.layers[il].bskcn_tv, 1, ggml_element_size(model.layers[il].bskcn_tv))));
++            }
++
++            // norm
++            cur = llm_build_norm(ctx0, inpL, hparams,
++                    model.layers[il].attn_norm, NULL,
++                    LLM_NORM_RMS, cb, il);
++            cb(cur, "attn_norm", il);
++
++            // self-attention
++            {
++                // rope freq factors for llama3; may return nullptr for llama2 and other models
++                struct ggml_tensor * rope_factors = build_rope_factors(il);
++
++                // compute Q and K and RoPE them
++                struct ggml_tensor * Qcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wq, cur);
++                cb(Qcur, "Qcur", il);
++                if (model.layers[il].bq) {
++                    Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq);
++                    cb(Qcur, "Qcur", il);
++                }
++
++                struct ggml_tensor * Kcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wk, cur);
++                cb(Kcur, "Kcur", il);
++                if (model.layers[il].bk) {
++                    Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk);
++                    cb(Kcur, "Kcur", il);
++                }
++
++                struct ggml_tensor * Vcur = llm_build_lora_mm(lctx, ctx0, model.layers[il].wv, cur);
++                cb(Vcur, "Vcur", il);
++                if (model.layers[il].bv) {
++                    Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv);
++                    cb(Vcur, "Vcur", il);
++                }
++
++                Qcur = ggml_rope_ext(
++                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, rope_factors,
++                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
++                    ext_factor, attn_factor, beta_fast, beta_slow
++                );
++                cb(Qcur, "Qcur", il);
++
++                Kcur = ggml_rope_ext(
++                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, rope_factors,
++                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
++                    ext_factor, attn_factor, beta_fast, beta_slow
++                );
++                cb(Kcur, "Kcur", il);
++
++                cur = llm_build_kv(ctx0, lctx, kv_self, gf,
++                        model.layers[il].wo, model.layers[il].bo,
++                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
++            }
++
++            if (il == n_layer - 1) {
++                // skip computing output for unused tokens
++                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
++                n_tokens = n_outputs;
++                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
++                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
++            }
++
++            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
++            cb(ffn_inp, "ffn_inp", il);
++
++            // feed-forward network
++            cur = llm_build_norm(ctx0, ffn_inp, hparams,
++                    model.layers[il].ffn_norm, NULL,
++                    LLM_NORM_RMS, cb, il);
++            cb(cur, "ffn_norm", il);
++
++            cur = llm_build_ffn(ctx0, lctx, cur,
++                    model.layers[il].ffn_up,   model.layers[il].ffn_up_b,   NULL,
++                    model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, NULL,
++                    model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL,
++                    NULL,
++                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
++            cb(cur, "ffn_out", il);
++
++            cur = ggml_add(ctx0, cur, ffn_inp);
++            cb(cur, "ffn_out", il);
++
++            cur = lctx.cvec.apply_to(ctx0, cur, il);
++            cb(cur, "l_out", il);
++
++            // input for next layer
++            inpL = cur;
++        }
++
++        cur = inpL;
++
++        cur = llm_build_norm(ctx0, cur, hparams,
++                model.output_norm, NULL,
++                LLM_NORM_RMS, cb, -1);
++        cb(cur, "result_norm", -1);
++
++        // lm_head
++        cur = llm_build_lora_mm(lctx, ctx0, model.output, cur);
++        cb(cur, "result_output", -1);
++
++        ggml_build_forward_expand(gf, cur);
++
++        return gf;
++    }
+ };
+ 
+ static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector<uint32_t> & ids) {
+@@ -15423,6 +15659,10 @@ static struct ggml_cgraph * llama_build_graph(
+             {
+                 result = llm.build_rwkv6();
+             } break;
++        case LLM_ARCH_SOLAR:
++            {
++                result = llm.build_solar();
++            } break;
+         default:
+             GGML_ABORT("fatal error");
+     }
+@@ -18503,6 +18743,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
+         case LLM_ARCH_ARCTIC:
+         case LLM_ARCH_DEEPSEEK2:
+         case LLM_ARCH_CHATGLM:
++        case LLM_ARCH_SOLAR:
+             return LLAMA_ROPE_TYPE_NORM;
+ 
+         // the pairs of head values are offset by n_rot/2
+-- 
+2.46.0
+

server/model.go

@@ -272,6 +272,30 @@ func detectContentType(r io.Reader) (string, error) {
 	return "unknown", nil
 }
 
+func parseObjects(s string) []map[string]any {
+	var objs []map[string]any
+	for offset := 0; offset < len(s); {
+		var obj map[string]any
+		decoder := json.NewDecoder(strings.NewReader(s[offset:]))
+		if err := decoder.Decode(&obj); errors.Is(err, io.EOF) || errors.Is(err, io.ErrUnexpectedEOF) {
+			break
+		} else if syntax := &(json.SyntaxError{}); errors.As(err, &syntax) {
+			// skip over any syntax errors
+			offset += int(syntax.Offset)
+		} else if unmarshalType := &(json.UnmarshalTypeError{}); errors.As(err, &unmarshalType) {
+			// skip over any unmarshalable types
+			offset += int(unmarshalType.Offset)
+		} else if err != nil {
+			return nil
+		} else {
+			offset += int(decoder.InputOffset())
+			objs = append(objs, obj)
+		}
+	}
+
+	return objs
+}
+
 // parseToolCalls attempts to parse a JSON string into a slice of ToolCalls.
 // mxyng: this only really works if the input contains tool calls in some JSON format
 func (m *Model) parseToolCalls(s string) ([]api.ToolCall, bool) {
@@ -304,16 +328,14 @@ func (m *Model) parseToolCalls(s string) ([]api.ToolCall, bool) {
 		return nil, false
 	}
 
-	var kv map[string]any
-	// execute the subtree with placeholders to identify the keys
-	// trim any commands that might exist in the template
-	if err := json.Unmarshal(bytes.TrimSuffix(b.Bytes(), []byte(",")), &kv); err != nil {
+	templateObjects := parseObjects(b.String())
+	if len(templateObjects) == 0 {
 		return nil, false
 	}
 
 	// find the keys that correspond to the name and arguments fields
 	var name, arguments string
-	for k, v := range kv {
+	for k, v := range templateObjects[0] {
 		switch v.(type) {
 		case string:
 			name = k
@@ -326,43 +348,32 @@ func (m *Model) parseToolCalls(s string) ([]api.ToolCall, bool) {
 		return nil, false
 	}
 
-	var objs []map[string]any
-	for offset := 0; offset < len(s); {
-		var obj map[string]any
-		decoder := json.NewDecoder(strings.NewReader(s[offset:]))
-		if err := decoder.Decode(&obj); errors.Is(err, io.EOF) || errors.Is(err, io.ErrUnexpectedEOF) {
-			break
-		} else if syntax := &(json.SyntaxError{}); errors.As(err, &syntax) {
-			// skip over any syntax errors
-			offset += int(syntax.Offset)
-		} else if unmarshalType := &(json.UnmarshalTypeError{}); errors.As(err, &unmarshalType) {
-			// skip over any unmarshalable types
-			offset += int(unmarshalType.Offset)
-		} else if err != nil {
-			slog.Error("parseToolCalls", "error", err)
-			return nil, false
-		} else {
-			offset += int(decoder.InputOffset())
+	responseObjects := parseObjects(s)
+	if len(responseObjects) == 0 {
+		return nil, false
+	}
 
-			// collect all nested objects
-			var collect func(any) []map[string]any
-			collect = func(obj any) (all []map[string]any) {
-				switch o := obj.(type) {
-				case map[string]any:
-					all = append(all, o)
-					for _, v := range o {
-						all = append(all, collect(v)...)
-					}
-				case []any:
-					for _, v := range o {
-						all = append(all, collect(v)...)
-					}
-				}
-
-				return all
+	// collect all nested objects
+	var collect func(any) []map[string]any
+	collect = func(obj any) (all []map[string]any) {
+		switch o := obj.(type) {
+		case map[string]any:
+			all = append(all, o)
+			for _, v := range o {
+				all = append(all, collect(v)...)
+			}
+		case []any:
+			for _, v := range o {
+				all = append(all, collect(v)...)
 			}
-			objs = append(objs, collect(obj)...)
 		}
+
+		return all
+	}
+
+	var objs []map[string]any
+	for _, p := range responseObjects {
+		objs = append(objs, collect(p)...)
 	}
 
 	var toolCalls []api.ToolCall

server/model_test.go

@@ -69,6 +69,7 @@ The temperature in San Francisco, CA is 70°F and in Toronto, Canada is 20°C.`,
 {"name": "get_current_weather", "arguments": {"format":"celsius","location":"Toronto, Canada"}}
 </tool_call>`, true},
 		{"xlam", `{"tool_calls": [{"name": "get_current_weather", "arguments": {"format":"fahrenheit","location":"San Francisco, CA"}},{"name": "get_current_weather", "arguments": {"format":"celsius","location":"Toronto, Canada"}}]}`, true},
+		{"nemotron", `<toolcall>{"name": "get_current_weather", "arguments": {"format":"fahrenheit","location":"San Francisco, CA"}},{"name": "get_current_weather", "arguments": {"format":"celsius","location":"Toronto, Canada"}}]} </toolcall>`, true},
 	}
 
 	var tools []api.Tool
@@ -217,3 +218,45 @@ func TestParseLayerFromCopy(t *testing.T) {
 		t.Fatalf("got %d != want 5", len(layers))
 	}
 }
+
+func TestParseObjects(t *testing.T) {
+	tests := []struct {
+		input string
+		want  []map[string]any
+	}{
+		{
+			input: `[{"name": "get_current_weather", "arguments": {"format":"fahrenheit","location":"San Francisco, CA"}},{"name": "get_current_weather", "arguments": {"format":"celsius","location":"Toronto, Canada"}}]`,
+			want: []map[string]any{
+				{"name": "get_current_weather", "arguments": map[string]any{"format": "fahrenheit", "location": "San Francisco, CA"}},
+				{"name": "get_current_weather", "arguments": map[string]any{"format": "celsius", "location": "Toronto, Canada"}},
+			},
+		},
+		{
+			input: `<toolcall>{"name": "get_current_weather", "arguments": {"format":"fahrenheit","location":"San Francisco, CA"}} </toolcall>`,
+			want: []map[string]any{
+				{"name": "get_current_weather", "arguments": map[string]any{"format": "fahrenheit", "location": "San Francisco, CA"}},
+			},
+		},
+		{
+			input: `<toolcall>{"name": "get_current_weather", "arguments": {"format":"fahrenheit","location":"San Francisco, CA"}} </toolcall> <toolcall>{"name": "get_current_weather", "arguments": {"format":"celsius","location":"Toronto, ON"}} </toolcall>`,
+			want: []map[string]any{
+				{"name": "get_current_weather", "arguments": map[string]any{"format": "fahrenheit", "location": "San Francisco, CA"}},
+				{"name": "get_current_weather", "arguments": map[string]any{"format": "celsius", "location": "Toronto, ON"}},
+			},
+		},
+		{
+			input: `{"name": "get_current_weather", "arguments": `,
+			want:  nil,
+		},
+	}
+
+	for _, tc := range tests {
+		t.Run(tc.input, func(t *testing.T) {
+			got := parseObjects(tc.input)
+
+			if diff := cmp.Diff(got, tc.want); diff != "" {
+				t.Errorf("mismatch (-got +want):\n%s", diff)
+			}
+		})
+	}
+}

server/testdata/tools/nemotron.gotmpl

@@ -0,0 +1,33 @@
+{{- if (or .Tools .System) }}<extra_id_0>System
+{{ if .System }}{{ .System }}
+
+
+{{ end }}
+{{- if .Tools }}
+{{- range .Tools }}<tool> {{ . }} </tool>{{ end }}
+
+
+{{ end }}
+{{- end }}
+{{- range $i, $m := .Messages }}
+{{- $last := eq (len (slice $.Messages $i)) 1 -}}
+{{- if eq .Role "user" }}<extra_id_1>User
+{{ .Content }}
+{{- if $last }}
+<extra_id_1>Assistant
+{{- end }}
+{{ else if eq .Role "tool" }}<extra_id_1>Tool
+{{ .Content }}
+{{- if $last }}
+<extra_id_1>Assistant
+{{- end }}
+{{ else if eq .Role "assistant" }}<extra_id_1>Assistant
+{{- if .ToolCalls }}
+{{ range .ToolCalls }}<toolcall> {"name": "{{ .Function.Name }}", "arguments": {{ .Function.Arguments }}} </toolcall> {{ end }}
+{{ else }}
+{{ .Content }}
+{{- if not $last }}
+{{ end }}
+{{- end }}
+{{- end }}
+{{- end }}

server/testdata/tools/nemotron.out

@@ -0,0 +1,18 @@
+<extra_id_0>System
+You are a knowledgable assistant. You can answer questions and perform tasks.
+
+
+<tool> {"type":"function","function":{"name":"get_current_weather","description":"Get the current weather","parameters":{"type":"object","required":["location","format"],"properties":{"format":{"type":"string","description":"The temperature unit to use. Infer this from the users location.","enum":["celsius","fahrenheit"]},"location":{"type":"string","description":"The city and state, e.g. San Francisco, CA"}}}}} </tool>
+
+
+<extra_id_1>User
+What's the weather like today in Paris?
+<extra_id_1>Assistant
+<toolcall> {"name": "get_current_weather", "arguments": {"format":"celsius","location":"Paris, France"}} </toolcall> 
+<extra_id_1>Tool
+22
+<extra_id_1>Assistant
+The current temperature in Paris, France is 22 degrees Celsius.
+<extra_id_1>User
+What's the weather like today in San Francisco and Toronto?
+<extra_id_1>Assistant