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Model: MorphMind-AI/CFM-Methods-3B Source: Original Platform
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LICENSE
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LICENSE
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MorphMind CFM Research License (v1.0)
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=====================================
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Copyright (c) 2026 MorphMind, Inc. All rights reserved.
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This model, "CFM-Methods-3B" (the "Model"), is released by MorphMind, Inc. for research and
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non-commercial use under the terms below.
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1. BASE MODEL. The Model is a fine-tune of Qwen2.5-3B-Instruct, licensed by Alibaba Cloud under the
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Qwen Research License. That license is incorporated by reference; use of the Model must comply with
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it and preserve attribution to Qwen.
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2. GRANT (RESEARCH / NON-COMMERCIAL). Subject to these terms, MorphMind grants you a worldwide,
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||||
non-exclusive, royalty-free, non-transferable license to use, reproduce, and create derivative works
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of the Model for research and other non-commercial purposes.
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3. ATTRIBUTION. Any use, publication, or derivative must credit both "MorphMind CFM-Methods-3B" and the
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"Qwen2.5" base model.
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4. COMMERCIAL USE. Commercial use is reserved. For a commercial license, contact MorphMind at
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https://morphmind.ai .
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5. INTENDED USE & DISCLAIMER. The Model is a high-recall screening tool. It is provided "AS IS",
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without warranty of any kind. Keep a qualified human in the loop.
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README.md
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README.md
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---
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license: other
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license_name: morphmind-cfm-research-license
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license_link: LICENSE
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base_model: Qwen/Qwen2.5-3B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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inference: false
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tags:
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- control-foundation-model
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- scientific-ai
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- methodology-review
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- peer-review
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- rlvr
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- morphmind
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---
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# CFM-Methods-3B · MorphMind
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**A tiny control model that reads a methods section and tells you exactly where the methodology is
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unsound.** Give it a methods or experimental-design block from any empirical-science paper ---
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**statistics, machine learning, quantitative biology, econometrics, materials science, or chemical
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physics** --- and it returns a structured verdict, **support** or **refute**, pinpoints the offending
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statement, and explains why. It is a **high-recall screen**: it surfaces methodological red flags ---
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data leakage, p-hacking, uncorrected multiple comparisons, train/test contamination, optional stopping,
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correlation-as-causation, post-hoc outlier removal, unblinded scoring, and more --- so a human misses
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almost nothing.
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At just **3B parameters**, CFM-Methods-3B delivers **frontier-level methodology screening** that runs
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on a single GPU, on-premise, at a tiny fraction of the cost of a frontier API. It is the compact member
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of MorphMind's **Control Foundation Model (CFM)** line --- models whose job is not to *generate*
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science but to **check** it.
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*By [MorphMind](https://morphmind.ai). Research preview.*
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## Benchmark --- methodology-flaw detection vs. frontier models
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Evaluated on **flaw types the model never trained on** (24 flaw families used for training, **12 held
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out for evaluation**), benchmarked head-to-head against frontier commercial models on the *same*
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held-out set:
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| Model | Recall | Precision | Localization | False-positive rate (clean) |
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|---|---|---|---|---|
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| base Qwen2.5-3B | 0.30 | --- | 0.42 | 0.07 |
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| GPT-4o | 0.86 | 0.64 | 0.94 | 0.47 |
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| Claude Opus 4 | 0.96 | 0.78 | 0.97 | 0.28 |
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| **CFM-Methods-3B (ours)** | **0.98** | **1.00** | **0.97** | **0.005** |
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**CFM-Methods-3B matches frontier recall and localization, with the cleanest false-alarm rate ---
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effectively zero.** It catches **98% of methodological flaws it has never seen** and pinpoints the exact
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flawed statement **97% of the time**, on par with Claude Opus 4 and GPT-4o, while the frontier models
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over-flag clean methods heavily (Opus 28%, GPT-4o 47% false-positive rate). So it delivers
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**frontier-grade methodology screening with the precision of a careful expert --- on-prem, in a 3B
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model, at a tiny fraction of the cost.**
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## Worked example
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**1 — it catches an uncorrected multiple-comparisons flaw.** Given this methods block:
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> *"We screened 60 candidate protein markers for association with disease status. Each marker was
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> tested individually with a univariate logistic regression at the 0.05 level. The 14 markers reaching
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> p < 0.05 in univariate tests are reported as significant and carried forward as the disease signature."*
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CFM-Methods-3B returns (verbatim model output):
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```json
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{
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"analysis": "There is a methodological flaw: Many tests without correction inflate the false-positive rate.",
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"verdict": "refute",
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"error_spans": [
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{
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"text": "The 14 markers reaching p < 0.05 in univariate tests are reported as significant",
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"why": "Many tests without correction inflate the false-positive rate."
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}
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],
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"action": "suggest_edit"
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}
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```
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It pinpoints the exact offending sentence and names the failure mode — 60 simultaneous tests at α = 0.05
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with no correction.
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**2 — and it passes clean methods without crying wolf.** Given a sound instrumental-variables design:
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> *"We estimate the causal effect of schooling on wages with two-stage least squares, instrumenting
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||||
> years of education with quarter-of-birth. We report first-stage F-statistics to confirm instrument
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> strength and cluster standard errors at the state level."*
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```json
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{
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"analysis": "The methodology is sound: instrument strength is verified and standard errors are clustered appropriately.",
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"verdict": "support",
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"error_spans": [],
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"action": "accept"
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}
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```
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No false flag — the near-zero false-positive rate in the benchmark above is what this looks like in practice.
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## When & how to use it
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Use it as a **fast, private, first-pass methodology screen** --- a pre-submission self-check for
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researchers, triage for journals / reviewers / grant panels, QA over a stack of submissions, or a check
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on AI-generated experimental designs. **Review one methods block at a time** (split a paper into its
|
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method / experiment / analysis sections and run each). Because it is tuned for recall, treat its flags
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as *"worth a human's 30 seconds."*
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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tok = AutoTokenizer.from_pretrained("MorphMind-AI/CFM-Methods-3B")
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model = AutoModelForCausalLM.from_pretrained("MorphMind-AI/CFM-Methods-3B",
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torch_dtype=torch.bfloat16, device_map="auto")
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SYS = ("You are a scientific methodology reviewer. Review the methods and respond ONLY with JSON: "
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"{\"analysis\":...,\"verdict\":\"support|refute\","
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"\"error_spans\":[{\"text\":...,\"why\":...}],\"action\":\"accept|suggest_edit\"}")
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def review(methods):
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msgs=[{"role":"system","content":SYS},{"role":"user","content":"METHODS:\n"+methods}]
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ids=tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
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out=model.generate(ids, max_new_tokens=320, do_sample=False)
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return tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True)
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```
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## How it was built
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A full-parameter fine-tune of Qwen2.5-3B-Instruct, trained with **RLVR** (Reinforcement Learning from
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Verifiable Rewards) under a **localization-gated reward** --- a verdict is reinforced only if the model
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also points to the actual flawed statement, which teaches genuine reasoning rather than blanket
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flagging. Trained on public **arXiv** methods sections across statistics, machine learning, quantitative
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biology, econometrics, materials science, and chemical physics, with injected, paraphrased
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methodological flaws; evaluated on held-out flaw families.
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## Notes
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- A **high-recall screen** for first-pass review: ~98% of flaws surfaced with a near-zero false-alarm
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rate, designed to keep an expert in the loop for the final call.
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- **Generalizes** to methodological flaws it has never seen, across six empirical-science families.
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- Part of MorphMind's growing **Control Foundation Model** family.
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## License
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||||
Released under the **MorphMind CFM Research License** (see `LICENSE`), incorporating the **Qwen Research
|
||||
License** of the Qwen2.5-3B base. Research / non-commercial use, with attribution to MorphMind and Qwen.
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||||
**For commercial licensing, contact MorphMind (morphmind.ai).**
|
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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'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\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>" }}
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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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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{{- '<|im_start|>assistant\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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"Qwen2ForCausalLM"
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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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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generation_config.json
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|
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|
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|
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|
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"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"model.norm.weight": "model-00002-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"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|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
|
||||
size 11421896
|
||||
207
tokenizer_config.json
Normal file
207
tokenizer_config.json
Normal file
@@ -0,0 +1,207 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"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
|
||||
}
|
||||
},
|
||||
"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 {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\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 {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.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' }}\n{%- endif %}\n",
|
||||
"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