初始化项目，由ModelHub XC社区提供模型

Model: olaverse/MIST-Mini-8B
Source: Original Platform

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README.md

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+---
+license: llama3.1
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+inference: true
+base_model:
+- NousResearch/Hermes-3-Llama-3.1-8B
+- NousResearch/DeepHermes-3-Llama-3-8B-Preview
+- nvidia/Llama-3.1-Nemotron-Nano-8B-v1
+- deepseek-ai/DeepSeek-R1-Distill-Llama-8B
+tags:
+- merge
+- dare_ties
+- llama
+- llama-3.1
+- mist
+---
+
+![mist models](https://cdn-uploads.huggingface.co/production/uploads/69949cbacd82af728f850c12/WzzrAklKUsaCCTTASicDf.png)
+
+# MIST-1-8B
+
+MIST-1-8B (formerly MIST-Mini) is the smallest and fastest model in the **MIST model family** by [olaverse](https://huggingface.co/olaverse). Built by blending 4 specialized Llama 3.1 8B models using DARE+TIES — delivering strong performance at maximum speed. 
+fast, thorough, great for everyday use
+
+## MIST Model Family
+
+| Model | Params | Speed | Status |
+|---|---|---|---|
+| **MIST-1-8B** | 8B | ~63 tok/s | ✅ Available |
+| [MIST-1-70B](https://huggingface.co/olaverse/MIST-1-70B) | 70B | ~23 tok/s | ✅ Available |
+| [MIST-1-140B](https://huggingface.co/olaverse/MIST-1-140B) | 140B | ~8 tok/s | ✅ Available |
+
+---
+
+## Key Strengths
+
+- ⚡ **Fastest** — 63 tok/s on H200, great for real-time applications
+- 🧠 **Strong Reasoning** — DeepSeek R1 distillation
+- 💻 **Clean Code** — production-ready with comments
+- 📐 **Math** — accurate step-by-step solving
+- 🤝 **Helpful** — low refusal rate
+- 📦 **Lightweight** — 15GB, runs on consumer GPUs
+
+---
+
+## Benchmark Results
+
+| Task | Speed | Quality |
+|---|---|---|
+| Reasoning | 4.5s | ✅ Correct |
+| Coding | 4.0s | ✅ Clean code |
+| Math | 4.0s | ✅ Step-by-step |
+| General | 4.0s | ✅ Accurate |
+| Instruction | 4.0s | ✅ Precise |
+
+**Average: 63 tok/s**
+
+---
+
+## How to Use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "olaverse/MIST-Mini-8B",
+    torch_dtype="auto",
+    device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("olaverse/MIST-Mini-8B")
+
+messages = [{"role": "user", "content": "Your question here"}]
+text = tokenizer.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+inputs = tokenizer(text, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Hardware Requirements
+
+| Precision | VRAM Required |
+|---|---|
+| bfloat16 | 16GB (RTX 3090/4090) |
+| 4-bit | 6GB (RTX 3060+) |
+
+---
+
+## Recommended Generation Settings
+
+These settings were verified through testing. Without `repetition_penalty` 
+and `min_p` the model will ramble and not stop cleanly.
+
+```python
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=1024,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.95,
+    min_p=0.05,
+    repetition_penalty=1.5,
+    eos_token_id=[128040, 128009, 128001],
+    pad_token_id=128001,
+)
+```
+
+### Stop Tokens
+This model's ChatML parents (`<|im_end|>`) survived the DARE+TIES merge 
+alongside Llama 3.1 native tokens. Use all three:
+
+| Token | ID | Source |
+|---|---|---|
+| `<\|im_end\|>` | 128040 | Hermes/Nemotron parents |
+| `<\|eot_id\|>` | 128009 | Llama 3.1 native |
+| `<\|end_of_text\|>` | 128001 | Llama 3.1 native |
+
+## License
+
+[Llama 3.1 Community License](https://llama.meta.com/llama3/license/)

chat_template.jinja

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+{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% set messages = messages[1:] %}{% else %}{% set system_message = 'You are a helpful assistant.' %}{% endif %}{{ '<|im_start|>system
+' + system_message + '<|im_end|>
+' }}{% for message in messages %}{% if message['role'] == 'user' %}{{ '<|im_start|>user
+' + message['content'] + '<|im_end|>
+' }}{% elif message['role'] == 'assistant' %}{{ '<|im_start|>assistant
+' + message['content'] + '<|im_end|>
+' }}{% elif message['role'] == 'tool' %}{{ '<|im_start|>tool
+' + message['content'] + '<|im_end|>
+' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
+' }}{% endif %}

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": [
+    128040,
+    128009,
+    128001
+  ],
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "eos_token_id": 128040,
+  "transformers_version": "4.57.6"
+}

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special_tokens_map.json

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templates/chat_template.jinja

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+' }}{% endif %}{{'<|im_start|>' + message['role'] + '
+' + message['content'] + '<|im_end|>' + '
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tokenizer.json

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