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Model: BillyWang1/qwen3-8b-tau-sft Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-8B
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datasets:
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- fuvty/tau-bench-synthetic
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- tau-bench
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- tool-calling
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- agent
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- sft
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- cold-start
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- qwen3
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---
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# qwen3-8b-tau-sft
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Non-thinking cold-start SFT of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) for
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[tau-bench](https://github.com/sierra-research/tau-bench) retail, trained on successful
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synthetic retail trajectories from
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[fuvty/tau-bench-synthetic](https://huggingface.co/datasets/fuvty/tau-bench-synthetic).
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This is the **epoch-1** checkpoint, which is the one that passed the pre-RL gates and was
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used to initialize GRPO. It is a *behavior-priming* checkpoint: it teaches the model to act
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as a non-thinking, one-tool-call-per-turn tau-bench agent. It is not a finished tau-bench
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policy on its own — see [Results](#results).
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## What "non-thinking" means here
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The tokenizer/chat template is a patched Qwen3 template whose assistant branch renders
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`<|im_start|>assistant\n<think>\n\n</think>\n\n` + content verbatim, and whose generation
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prompt ends with that same empty-think prefill. Because `</think>` is therefore *prompt-side*,
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think-relapse is structurally impossible even at temperature 1.0. The patched
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`chat_template.jinja` and the embedded copy in `tokenizer_config.json` ship with this repo,
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so `apply_chat_template` reproduces the training format exactly.
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Tool calls are emitted as inline Hermes text in the assistant content (the patched template
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does **not** render structured `tool_calls` lists):
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```
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{optional user-visible text}
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<tool_call>
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{"name": "<fn>", "arguments": {<args object>}}
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</tool_call>
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```
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At most one call per turn. Tool results are supplied as `role: "tool"` messages, which the
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template wraps in `<tool_response>…</tool_response>` inside a user turn.
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## Training data
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795 reward-1.0 retail trajectories from `traj-GLM5` were filtered down to **595 rows over 98
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tasks**:
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- airline dropped entirely (it is a held-out zero-shot transfer eval)
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- **12 of 180** synthetic retail tasks dropped by a contamination gate that compares
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write-action goal signatures against the shipped tau-bench retail task sets. All 12 were
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retail-**train** collisions; **zero** collided with retail-**test**
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- 135 trajectories dropped for containing a turn with more than one tool call
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- reasoning traces (`reasoning_content`) stripped; the tau2 greeting turn dropped
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Loss falls on exactly `assistant content + <|im_end|>`. The assistant prefill header, the
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inter-turn newline and every observation block are present but masked out, so the training
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token stream is byte-identical to what an inference server is fed.
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## Training
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| | |
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|---|---|
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| base | Qwen/Qwen3-8B |
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| framework | slime 0.3.0 (Megatron), 4×H100 |
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| rows / batch | 595 / 32 |
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| steps | 54 (18 per epoch × 3); **this repo is epoch 1 = 18 steps** |
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| lr | 1e-5 cosine, min 1e-6, warmup 0.1 |
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| parallelism | TP2, seq-parallel, bf16 |
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| loss | token-level SFT on pre-tokenized rows |
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Final loss 0.118 at epoch 3; this epoch-1 checkpoint was selected because it already passed
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the boundary-token gate below and further epochs were not needed for priming.
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## Gates
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**Boundary-token probe** (does SFT actually train the turn-ending specials?):
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| bucket | n | mean p | median | frac < 0.5 |
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|---|---|---|---|---|
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| `<\|im_end\|>` | 64 | **0.9625** | 0.9983 | 1.6% |
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| `<tool_call>` / `</tool_call>` | 62 | 0.9786 | 0.9998 | 0.0% |
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| content | 7108 | 0.8867 | 0.9994 | 10.0% |
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**Format/geometry gate**: converted rows were replayed through the actual multi-turn rollout
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code with the row's turns scripted as sampled outputs, asserting exact-token prefix alignment
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of the generation prompt at every turn, loss-token equivalence, tool-parser round-trip, and
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eos invariants — **595/595 passed**. Longest assistant turn: 613 tokens.
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## Results
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Evaluated with **greedy** decoding on tau-bench retail-test (115 tasks) and airline (50 tasks,
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never trained on), with a `gpt-4.1-mini` user simulator.
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| | retail-test | airline |
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|---|---|---|
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| **this checkpoint (SFT only)** | 0.043 | 0.020 |
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| after 32 GRPO steps from this init | **0.322** | 0.060 |
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| Qwen3-8B *thinking*, zero-shot (reference) | 0.391 | 0.240 |
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Sampled (T=1.0, 4 samples/task) after those 32 GRPO steps, where the decoding pathology
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is absent:
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| | pass@1 | pass@2 | pass@4 |
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|---|---|---|---|
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| retail-test | 0.315 | 0.443 | 0.574 |
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| airline (never trained on) | 0.140 | 0.237 | 0.360 |
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**Read the SFT-only number carefully.** 0.043 greatly understates the policy: under sampling
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(T=1.0) the same weights score **0.637** average success on retail-train with
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**pass@16 ≈ 0.97**. The gap is a greedy-decoding pathology — 93% of greedy eval episodes
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exhaust their 30-turn budget in a repetition loop rather than terminating. Subsequent GRPO
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mostly *repaired that pathology* (greedy truncation 0.93 → 0.33, repetition 0.48 → 0.15)
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rather than adding raw capability, which is why greedy retail-test climbs 7.5× while sampled
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training reward barely moves.
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So: this checkpoint is a strong **sampled** tool-use policy and a weak **greedy** one. Use
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sampling, or RL from it.
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## Intended use and limitations
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Research artifact for tau-bench agent RL. Not a general assistant, not tuned for safety or
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chat. Its system prompt at training time was the synthetic dataset's own
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`<instructions>/<policy>` wrapper and its tool schemas were the dataset's tau2-style retail
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variant, which differ in three tool names from tau-bench's own retail toolset
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(`cancel_order` vs `cancel_pending_order`, `find_user_by_contact` vs `find_user_id_by_email`,
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`find_user_by_name` vs `find_user_id_by_name_zip`). In practice the model conditions on
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whatever schemas are given in context and transferred to the real tau-bench names with
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essentially no penalty (5 parse failures across 165 eval episodes).
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## License
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Apache-2.0, inherited from both Qwen3-8B and the tau-bench-synthetic dataset.
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role + '\n<think>\n\n</think>\n\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n<think>\n\n</think>\n\n' }}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 12288,
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"max_position_embeddings": 40960,
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"max_window_layers": 36,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.51.0",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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"pad_token_id": 151643,
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"temperature": 0.6,
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"top_k": 20,
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"top_p": 0.95,
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"transformers_version": "4.51.0"
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}
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|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n\\n</think>\\n\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n<think>\\n\\n</think>\\n\\n' }}\n{%- endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user