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Model: reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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pipeline_tag: text-generation
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license: apache-2.0
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language:
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- en
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base_model: Qwen/Qwen3-0.6B
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datasets:
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- 0xZee/dataset-CoT-Advanced-Calculus-268
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- 0xZee/dataset-CoT-Modern-Physics-177
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- 0xZee/dataset-CoT-Theoretical-Mechanics-307
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- 0xZee/dataset-CoT-Linear-Algebra-667
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- 0xZee/dataset-CoT-Electromagnetism-580
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- 0xZee/dataset-CoT-Molecular-Biology-71
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- 0xZee/dataset-CoT-Physiology-114
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- 0xZee/dataset-CoT-Classical-Mechanics-343
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- 0xZee/dataset-CoT-Differential-Equations-636
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- 0xZee/dataset-CoT-Physics-2254
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- 0xZee/dataset-CoT-Engineering-574
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- 0xZee/dataset-CoT-mathematics
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- Alignment-Lab-AI/Lawyer-Instruct
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tags:
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- causal-lm
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- text-generation
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- distillation
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- knowledge-distillation
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- sft
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- reasoning
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- chain-of-thought
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- mathematics
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- physics
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- engineering
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- legal
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- stem
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- convergentintel
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- edge
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---
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# Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT
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A 0.6B parameter model built in two stages: knowledge distillation from a 30B Thinking teacher to establish a structured reasoning backbone, then supervised fine-tuning on legal instruction data. 50x compression. Under 500MB quantized. Runs on a phone.
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The training order is the thesis: teach the model *how to reason* first (distillation from Thinking teacher), then teach it *what to reason about* (legal SFT). The Thinking teacher's extended deliberation traces transfer deeper reasoning structure than an Instruct teacher — critical when the student has only 0.6B parameters to work with.
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> *"Structure beats scale, collaboration beats hierarchy, observation beats theory."*
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> — Convergent Intelligence LLC: Research Division
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## Training Pipeline
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### Stage 1: Knowledge Distillation (STEM Reasoning Backbone)
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Qwen3-0.6B distilled from [Qwen3-30B-A3B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507) — a Mixture-of-Experts model with 30B total parameters, ~3B active per token, using the Thinking variant that generates extended internal reasoning traces.
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**Why the Thinking teacher matters at 0.6B:** The Thinking variant produces higher-entropy softmax distributions than the Instruct variant — it considers more reasoning paths before committing. At distillation temperature T=2.0, the 0.6B student sees a richer landscape of alternative derivation strategies. With only 0.6B parameters, every bit of transferred structure counts. The Thinking teacher gives more.
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**Data:** 6,122 STEM chain-of-thought samples across 12 domains:
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| Domain | Samples |
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|---|---|
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| Physics | 2,254 |
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| Linear Algebra | 667 |
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| Differential Equations | 636 |
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| Electromagnetism | 580 |
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| Mathematics | 576 |
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| Engineering | 574 |
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| Classical Mechanics | 343 |
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| Theoretical Mechanics | 307 |
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| Advanced Calculus | 268 |
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| Modern Physics | 177 |
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| Physiology | 114 |
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| Molecular Biology | 71 |
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All from [0xZee](https://huggingface.co/0xZee). Shuffled seed 42, split 95/5 train/eval.
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**Loss function:**
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1. **Proof-Weighted Cross-Entropy (55%)** — 2.5x weight on derivation tokens, decaying to 1.5x. Forces the student to allocate its limited capacity to reasoning steps, not answer formatting.
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2. **Knowledge Distillation KL Divergence (45%)** — T=2.0, scaled by T². Transfers the Thinking teacher's full deliberation landscape.
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**Training format:**
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```
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Solve the following problem carefully and show a rigorous derivation.
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Problem:
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{question}
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Proof:
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{CoT}
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Final Answer:
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{response}
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```
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**Stage 1 hyperparameters:**
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| Parameter | Value |
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|---|---|
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| Epochs | 1 |
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| Training samples | 5,815 |
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| Effective batch size | 8 |
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| Learning rate | 1.5e-5 → 1e-6 (cosine) |
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| Temperature | 2.0 |
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| Proof weight | 2.5 → 1.5 |
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| Precision | bf16 |
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---
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### Stage 2: Supervised Fine-Tuning (Legal Domain)
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The distilled model was fine-tuned on [Alignment-Lab-AI/Lawyer-Instruct](https://huggingface.co/datasets/Alignment-Lab-AI/Lawyer-Instruct) using TRL's SFTTrainer.
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**Why legal on top of STEM:** Legal reasoning is structurally isomorphic to mathematical reasoning — premise identification, logical chaining, exception handling, structured argumentation toward a conclusion. A model that learned rigorous derivation transfers that structure to legal analysis rather than learning legal templates from scratch.
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**Training format:**
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```
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### Instruction:
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{instruction}
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### Response:
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{output}
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```
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**Stage 2 hyperparameters:**
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| Parameter | Value |
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|---|---|
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| Epochs | 1 |
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| Effective batch size | 8 |
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| Learning rate | 5e-6 (lower than Stage 1 to preserve backbone) |
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| Gradient checkpointing | Enabled |
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| Precision | bf16 |
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## Model Details
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| Attribute | Value |
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|---|---|
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| **Architecture** | Qwen3 (causal LM, RoPE, GQA) |
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| **Parameters** | 0.6B |
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| **Base model** | [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) |
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| **Teacher model** | [Qwen/Qwen3-30B-A3B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Thinking-2507) |
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| **Compression ratio** | 50x |
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| **Stage 1 data** | 6,122 STEM CoT samples (12 datasets) |
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| **Stage 2 data** | [Alignment-Lab-AI/Lawyer-Instruct](https://huggingface.co/datasets/Alignment-Lab-AI/Lawyer-Instruct) |
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| **Context length** | 1024 tokens (training) |
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| **License** | Apache 2.0 |
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| **Developer** | Reaperdoesntrun / [Convergent Intelligence LLC](https://convergentintel.com): Research Division |
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## Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
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device_map="auto",
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)
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# Legal instruction-following
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prompt = """### Instruction:
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What is the difference between a felony and a misdemeanor?
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### Response:
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"""
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# STEM derivation (Stage 1 format still works)
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prompt_stem = """Solve the following problem carefully and show a rigorous derivation.
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Problem:
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Compute the determinant of the matrix [[1, 2], [3, 4]].
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Proof:
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"""
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### GGUF
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Quantized versions at [reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF).
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## Prompt Formats
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**STEM derivation (Stage 1):**
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```
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Solve the following problem carefully and show a rigorous derivation.
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Problem:
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[Your problem]
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Proof:
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```
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**Instruction-following (Stage 2):**
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```
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### Instruction:
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[Your question]
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### Response:
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```
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## Intended Uses
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**Good for:** Ultra-lightweight reasoning on mobile/edge/IoT, legal and STEM instruction-following, educational tutoring, embedded inference, component in multi-model pipelines, anywhere you need reasoning in under 500MB.
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**Not for:** Formal proof verification, actual legal counsel, safety-critical analysis, complex multi-step proofs (>8 steps), or long-context tasks beyond 1024 tokens.
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## Limitations
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0.6B is a hard capacity constraint. The model trades depth for deployability. It will make reasoning errors that a larger model would not. Multi-step derivations beyond ~8 steps degrade. Legal reasoning covers general concepts but lacks the nuance of larger models. Performance is weakest on underrepresented domains (molecular biology, physiology). Always verify outputs.
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## Mathematical Foundations: Discrepancy Calculus (DISC)
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This model is part of a distillation chain built on Discrepancy Calculus — a measure-theoretic framework where the teacher's output distribution is decomposed via the Mesh Fundamental Identity into smooth (AC), jump, and Cantor components. The discrepancy operator $Df(x) = \lim_{\varepsilon \downarrow 0} \frac{1}{\varepsilon} \int_x^{x+\varepsilon} \frac{|f(t) - f(x)|}{|t - x|} dt$ quantifies local structural mismatch that standard KL divergence averages away.
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Full theory: *"On the Formal Analysis of Discrepancy Calculus"* (Colca, 2026; Convergent Intelligence LLC: Research Division). Full methodology: [Structure Over Scale (DOI: 10.57967/hf/8165)](https://doi.org/10.57967/hf/8165).
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## Related Models
|
||||
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| Model | Description |
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|---|---|
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| [Qwen3-0.6B-STEM-Proof-Distilled-Thinking](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-STEM-Proof-Distilled-Thinking) | Stage 1 only — pure STEM backbone |
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| [Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF) | This model quantized for edge deployment |
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| [Qwen3-1.7B-STEM-Proof-Distilled](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-STEM-Proof-Distilled) | Larger 1.7B variant (Instruct teacher) |
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| [Qwen3-1.7B-Distilled-30B-A3B-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT) | Larger 1.7B variant + legal SFT |
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## Citation
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```bibtex
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@misc{colca2026thinking06bsft,
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title={Two-Stage Reasoning Transfer at 0.6B: Thinking Teacher Distillation + Legal SFT},
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year={2026},
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publisher={HuggingFace},
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url={https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT},
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note={Convergent Intelligence LLC: Research Division}
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}
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```
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---
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*Convergent Intelligence LLC: Research Division*
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*"Where classical analysis fails to see, we begin."*
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---
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## Convergent Intelligence Portfolio
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*Part of the [Qwen3 0.6B Distillation Series](https://huggingface.co/reaperdoesntknow) by [Convergent Intelligence LLC: Research Division](https://huggingface.co/reaperdoesntknow)*
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#
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## Mathematical Foundations: Discrepancy Calculus (DISC)
|
||||
|
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This model is part of a distillation chain built on Discrepancy Calculus — a measure-theoretic framework where the teacher's output distribution is decomposed via the Mesh Fundamental Identity into smooth (AC), jump, and Cantor components. The discrepancy operator $Df(x) = \lim_{\varepsilon \downarrow 0} \frac{1}{\varepsilon} \int_x^{x+\varepsilon} \frac{|f(t) - f(x)|}{|t - x|} dt$ quantifies local structural mismatch that standard KL divergence averages away.
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|
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Full theory: *"On the Formal Analysis of Discrepancy Calculus"* (Colca, 2026; Convergent Intelligence LLC: Research Division). Full methodology: [Structure Over Scale (DOI: 10.57967/hf/8165)](https://doi.org/10.57967/hf/8165).
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## Related Models
|
||||
|
||||
| Model | Downloads | Format |
|
||||
|-------|-----------|--------|
|
||||
| [Qwen3-0.6B-Distilled-30B-A3B](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B) | 36 | HF |
|
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| [Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF) | 203 | GGUF |
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### Top Models from Our Lab
|
||||
|
||||
| Model | Downloads |
|
||||
|-------|-----------|
|
||||
| [Qwen3-1.7B-Thinking-Distil](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Thinking-Distil) | 501 |
|
||||
| [LFM2.5-1.2B-Distilled-SFT](https://huggingface.co/reaperdoesntknow/LFM2.5-1.2B-Distilled-SFT) | 342 |
|
||||
| [Qwen3-1.7B-Coder-Distilled-SFT](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT) | 302 |
|
||||
| [Qwen3-1.7B-Coder-Distilled-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT-GGUF) | 194 |
|
||||
| [Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF) | 175 |
|
||||
|
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**Total Portfolio: 41 models | 2,781 total downloads**
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*Last updated: 2026-03-28 12:56 UTC*
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<!-- DISTILQWEN-SPOTLIGHT-START -->
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## DistilQwen Collection
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This model is part of the **[DistilQwen](https://huggingface.co/collections/reaperdoesntknow/distilqwen-69bf40ec669117e3f069ef1c)** proof-weighted distillation series.
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Collection: **9 models** | **2,788 downloads**
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### Teacher Variant Comparison
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| Teacher | Student Size | Strength | Models |
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|---------|-------------|----------|--------|
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| Qwen3-30B-A3B (Instruct) | 1.7B | Instruction following, structured output, legal reasoning | 3 (833 DL) |
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| Qwen3-30B-A3B (Thinking) | 0.6B | Extended deliberation, higher-entropy distributions, proof derivation | 3 (779 DL) **← this model** |
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| Qwen3-30B-A3B (Coder) | 1.7B | Structured decomposition, STEM derivation, logical inference | 2 (825 DL) |
|
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|
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### Methodology
|
||||
|
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**The only BF16 collection in the portfolio.** While the broader Convergent Intelligence catalog (43 models, 12,000+ downloads) was trained on CPU at FP32 for $24 total compute, the DistilQwen series was trained on H100 at BF16 with a 30B-parameter teacher. Same methodology, premium hardware. This is what happens when you give the pipeline real compute.
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All models use proof-weighted knowledge distillation: 55% cross-entropy with decaying proof weights (2.5× → 1.5×), 45% KL divergence at T=2.0. The proof weight amplifies loss on reasoning-critical tokens, forcing the student to allocate capacity to structural understanding rather than surface-level pattern matching.
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Full methodology: [Structure Over Scale (DOI: 10.57967/hf/8165)](https://doi.org/10.57967/hf/8165)
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||||
### Related in this series
|
||||
|
||||
- [Qwen3-0.6B-Distilled-30B-A3B](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B) (236 downloads)
|
||||
- [Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF](https://huggingface.co/reaperdoesntknow/Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF) (316 downloads)
|
||||
|
||||
<!-- DISTILQWEN-SPOTLIGHT-END -->
|
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<!-- cix-keeper-ts:2026-06-12T13:16:20Z -->
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<!-- card-refresh: 2026-03-30 -->
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89
chat_template.jinja
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89
chat_template.jinja
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||||
{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{{- messages[0].content + '\n\n' }}
|
||||
{%- endif %}
|
||||
{{- "# 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>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\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" }}
|
||||
{%- else %}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
||||
{%- for message in messages[::-1] %}
|
||||
{%- set index = (messages|length - 1) - loop.index0 %}
|
||||
{%- 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>')) %}
|
||||
{%- set ns.multi_step_tool = false %}
|
||||
{%- set ns.last_query_index = index %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- for message in messages %}
|
||||
{%- if message.content is string %}
|
||||
{%- set content = message.content %}
|
||||
{%- else %}
|
||||
{%- set content = '' %}
|
||||
{%- endif %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{%- set reasoning_content = '' %}
|
||||
{%- if message.reasoning_content is string %}
|
||||
{%- set reasoning_content = message.reasoning_content %}
|
||||
{%- else %}
|
||||
{%- if '</think>' in content %}
|
||||
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
||||
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- if loop.index0 > ns.last_query_index %}
|
||||
{%- if loop.last or (not loop.last and reasoning_content) %}
|
||||
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- if message.tool_calls %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content) or (not loop.first) %}
|
||||
{{- '\n' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{%- endif %}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- if enable_thinking is defined and enable_thinking is false %}
|
||||
{{- '<think>\n\n</think>\n\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
63
config.json
Normal file
63
config.json
Normal file
@@ -0,0 +1,63 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": null,
|
||||
"dtype": "bfloat16",
|
||||
"eos_token_id": 151645,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 1024,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 3072,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 40960,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 8,
|
||||
"pad_token_id": 151643,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_parameters": {
|
||||
"rope_theta": 1000000,
|
||||
"rope_type": "default"
|
||||
},
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"transformers_version": "5.0.0",
|
||||
"use_cache": false,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
12
generation_config.json
Normal file
12
generation_config.json
Normal file
@@ -0,0 +1,12 @@
|
||||
{
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
151645,
|
||||
151643
|
||||
],
|
||||
"pad_token_id": 151643,
|
||||
"temperature": 0.6,
|
||||
"top_k": 20,
|
||||
"top_p": 0.95,
|
||||
"transformers_version": "5.0.0"
|
||||
}
|
||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:c1d2b15ffb4239c2d83a24ca59e35b8cb427a03dcdc3c667bba985e64907b258
|
||||
size 1503300328
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
|
||||
size 11422650
|
||||
29
tokenizer_config.json
Normal file
29
tokenizer_config.json
Normal file
@@ -0,0 +1,29 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_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|>"
|
||||
],
|
||||
"is_local": false,
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
Reference in New Issue
Block a user