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Model: hvss/Dispatch-7B Source: Original Platform
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---
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license: apache-2.0
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language:
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- en
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base_model: mistralai/Mistral-7B-Instruct-v0.3
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datasets:
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- hvss/dispatch-7b-data
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pipeline_tag: text-generation
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tags:
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- agent
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- router
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- orchestration
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- tool-use
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- planning
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- unsloth
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- mistral
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---
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# The Dispatcher: Agent Ops 🚦
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> **Dispatch-7B, an agent orchestrator in 7B parameters — the tiny AI manager that runs a team of expensive AI workers.**
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🎬 **[Watch it route work live →](https://huggingface.co/spaces/hvss/dispatch-7b-demo)**
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Dispatch-7B is a fine-tuned **Mistral 7B Instruct v0.3** that acts as the **orchestrator** in an agentic system. It reads a user request plus a catalog of available tools and outputs a structured execution plan: which tool handles each step, in what order (as a dependency DAG), and which steps genuinely deserve escalation to a frontier model.
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**Small model as the boss. Big models as the workers.**
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Most AI products send *every* request — even trivial ones — to the biggest, most expensive model. That's like having your highest-paid executive answer the front desk phone. Dispatch-7B is the dispatcher: it plans in milliseconds on cheap hardware, routes each step to the cheapest adequate worker, and escalates only when a step actually requires frontier-level reasoning.
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## Evaluation
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Measured on 114 held-out tasks (unseen requests *and* unseen tool-catalog combinations):
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| Metric | Score |
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|---|---|
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| Valid plan rate (parseable JSON, schema-correct, acyclic DAG, catalog-consistent) | **97.4%** |
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| Tool selection F1 (vs. teacher plans) | **0.92** |
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| Escalation precision / recall | **0.92 / 0.75** |
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| Est. cost vs. "frontier model does everything" | **−86.7%** |
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Robustness breakdowns:
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| Slice | Valid plan rate |
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|---|---|
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| Distractor-heavy catalogs (relevant tools buried among irrelevant ones) | 93% |
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| Gap catalogs (a needed tool deliberately missing) | 94% |
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| Hard tasks requiring escalation judgment | 89% |
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Notes: routing labels are distilled from a teacher model (gpt-5-mini), so accuracy metrics measure agreement with the teacher. The cost figure is a transparent cost *model* over the eval plans (frontier $3/$15 per MTok, mini-tier $0.15/$0.60, self-hosted 7B router; invalid router plans charged as full frontier fallback), not a measured bill — assumptions are in the [eval scripts](https://huggingface.co/datasets/hvss/dispatch-7b-data).
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The model deliberately **under-escalates** slightly (11.7% of plans vs. the teacher's 15.8%): it errs on the side of cheap execution, which is usually the right default for a router.
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## Usage
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The model expects Mistral's `[INST]` format with the dispatcher system prompt and your tool catalog:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("hvss/Dispatch-7B", torch_dtype="auto", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("hvss/Dispatch-7B")
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SYSTEM = '''You are a dispatcher. Read the user request and the tool catalog, then output ONLY a JSON execution plan routing each step to the cheapest adequate worker: "tool:<name>" (catalog tools only), "small_model" (simple generation), or "escalate:frontier" (genuinely hard reasoning only). Steps form a DAG via "depends_on". Output format: {"goal": ..., "steps": [{"id", "task", "assignee", "depends_on"}], "escalation_reason": ...}
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Available tools:
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- weather_lookup: Get the weather forecast for a location.
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- calendar_create_event: Create a calendar event with title, time, and attendees.
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- email_send: Send an email to one or more recipients.'''
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request = "Set up a team picnic next Friday at noon if the weather looks good, and email the team an invite."
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prompt = f"[INST] {SYSTEM}\n\n{request} [/INST]"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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out = model.generate(**inputs, max_new_tokens=768, do_sample=False)
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print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
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```
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Output:
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```json
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{"goal": "...", "steps": [
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{"id": 1, "task": "Check the weather forecast for next Friday.", "assignee": "tool:weather_lookup", "depends_on": []},
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{"id": 2, "task": "Create the picnic calendar event if the forecast is good.", "assignee": "tool:calendar_create_event", "depends_on": [1]},
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{"id": 3, "task": "Email the team an invite with a short fun blurb.", "assignee": "tool:email_send", "depends_on": [2]}
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], "escalation_reason": null}
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```
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Three assignee classes:
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| Assignee | Meaning | Cost |
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|---|---|---|
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| `tool:<name>` | Direct tool/API call from your catalog | ~free |
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| `small_model` | Simple generation a small LLM handles fine | cheap |
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| `escalate:frontier` | Genuinely hard reasoning — send to the big model | expensive, used sparingly |
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Swap in **your own catalog** — the model was trained on randomized catalogs (with distractors and deliberate gaps), so it plans with whatever tools you give it rather than assuming a fixed set.
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## Variants
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| Repo | Use case |
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|---|---|
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| [hvss/Dispatch-7B](https://huggingface.co/hvss/Dispatch-7B) | merged fp16 — vLLM / transformers |
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| [hvss/Dispatch-7B-GGUF](https://huggingface.co/hvss/Dispatch-7B-GGUF) | Q4_K_M / Q8_0 — llama.cpp, Ollama, local |
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| [hvss/Dispatch-7B-LoRA](https://huggingface.co/hvss/Dispatch-7B-LoRA) | adapter only — further fine-tuning |
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## Training
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- **Base**: Mistral 7B Instruct v0.3 (Apache 2.0)
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- **Method**: QLoRA (4-bit, r=16 on all attention + MLP projections) via Unsloth, on a single free Colab T4
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- **Data**: 2,158 (request, gold plan) pairs distilled from a teacher model over a structured taxonomy: 8 domains × 4 difficulty tiers × 3 catalog conditions (full / distractor-heavy / deliberately-missing-tool), ~16% escalation rate to teach restraint
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- **Loss**: response-only (the model learns to write plans, not to regenerate catalogs)
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## Limitations
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- English-only training data.
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- Routing quality is bounded by the teacher's judgment; gold labels are synthetic.
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- The tool catalogs are synthetic (40 tools across 8 domains); very different domains or tool-description styles may need a light further fine-tune (start from the [LoRA repo](https://huggingface.co/hvss/Dispatch-7B-LoRA)).
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- The model plans; it does not execute. Your harness owns tool execution, worker calls, and validation (schema-check the output — 2.6% of plans fail validation and should fall back to your default model).
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## License
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Apache 2.0 — free for commercial use.
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{%- if messages[0]["role"] == "system" %}
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{%- set system_message = messages[0]["content"] %}
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{%- set loop_messages = messages[1:] %}
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{%- else %}
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{%- set loop_messages = messages %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{%- set user_messages = loop_messages | selectattr("role", "equalto", "user") | list %}
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{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}
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{%- set ns = namespace() %}
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{%- set ns.index = 0 %}
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{%- for message in loop_messages %}
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{%- if not (message.role == "tool" or message.role == "tool_results" or (message.tool_calls is defined and message.tool_calls is not none)) %}
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{%- if (message["role"] == "user") != (ns.index % 2 == 0) %}
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{{- raise_exception("After the optional system message, conversation roles must alternate user/assistant/user/assistant/...") }}
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{%- endif %}
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{%- set ns.index = ns.index + 1 %}
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{%- endif %}
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{%- endfor %}
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{{- bos_token }}
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{%- for message in loop_messages %}
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{%- if message["role"] == "user" %}
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{%- if tools is not none and (message == user_messages[-1]) %}
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{{- "[AVAILABLE_TOOLS] [" }}
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{%- for tool in tools %}
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{%- set tool = tool.function %}
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{{- '{"type": "function", "function": {' }}
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{%- for key, val in tool.items() if key != "return" %}
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{%- if val is string %}
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{{- '"' + key + '": "' + val + '"' }}
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{%- else %}
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{{- '"' + key + '": ' + val|tojson }}
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{%- endif %}
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{%- if not loop.last %}
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{{- ", " }}
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{%- endif %}
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{%- endfor %}
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{{- "}}" }}
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{%- if not loop.last %}
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{{- ", " }}
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{%- else %}
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{{- "]" }}
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{%- endif %}
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{%- endfor %}
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{{- "[/AVAILABLE_TOOLS]" }}
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{%- endif %}
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{%- if loop.last and system_message is defined %}
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{{- "[INST] " + system_message + "\n\n" + message["content"] + "[/INST]" }}
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{%- else %}
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{{- "[INST] " + message["content"] + "[/INST]" }}
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{%- endif %}
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{%- elif message.tool_calls is defined and message.tool_calls is not none %}
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{{- "[TOOL_CALLS] [" }}
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{%- for tool_call in message.tool_calls %}
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{%- set out = tool_call.function|tojson %}
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{{- out[:-1] }}
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{%- if not tool_call.id is defined or tool_call.id|length != 9 %}
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{{- raise_exception("Tool call IDs should be alphanumeric strings with length 9!") }}
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{%- endif %}
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{{- ', "id": "' + tool_call.id + '"}' }}
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{%- if not loop.last %}
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{{- ", " }}
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{%- else %}
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{{- "]" + eos_token }}
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{%- endif %}
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{%- endfor %}
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{%- elif message["role"] == "assistant" %}
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{{- " " + message["content"]|trim + eos_token}}
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{%- elif message["role"] == "tool_results" or message["role"] == "tool" %}
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{%- if message.content is defined and message.content.content is defined %}
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{%- set content = message.content.content %}
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{%- else %}
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{%- set content = message.content %}
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{%- endif %}
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{{- '[TOOL_RESULTS] {"content": ' + content|string + ", " }}
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{%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}
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{{- raise_exception("Tool call IDs should be alphanumeric strings with length 9!") }}
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{%- endif %}
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{{- '"call_id": "' + message.tool_call_id + '"}[/TOOL_RESULTS]' }}
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{%- else %}
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{{- raise_exception("Only user and assistant roles are supported, with the exception of an initial optional system message!") }}
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{%- endif %}
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{%- endfor %}
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config.json
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{
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"torch_dtype": "float16",
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"eos_token_id": 2,
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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": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
|
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pad_token_id": 770,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": false,
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"unsloth_version": "2026.7.4",
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"use_cache": false,
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"vocab_size": 32768
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}
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"transformers_version": "5.5.0"
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275733
tokenizer.json
Normal file
275733
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
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oid sha256:37f00374dea48658ee8f5d0f21895b9bc55cb0103939607c8185bfd1c6ca1f89
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||||
size 587404
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6188
tokenizer_config.json
Normal file
6188
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user