HachiML
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SkillTree-Chat-Mistral-7B-v0.1

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-## Model Details
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 ---
 library_name: transformers
+tags:
+- SkillTree
+- mistral
+license: apache-2.0
 ---
+# SkillTree Model Collection
+Applying a skill to your model with SkillTree is akin to unlocking a new ability in a video game's skill tree. Just as you would enhance your character's capabilities by selecting and activating specific skills, you can augment your model's abilities by integrating specialized skills. Follow these steps to imbue your model with new prowess, enhancing its performance and versatility in a straightforward and intuitive manner.
+**Please note that SkillTree abilities may not function in all cases. To determine whether a specific skill is operational, refer to the Functionality Status.**
+## What is SkillTree?
+SkillTree represents a set of model weights derived from further pre-training or fine-tuning Large Language Models (LLMs) to extract specific capabilities, such as code generation or chatting abilities. These extracted "skills" can be combined with a specific LLM base model to enhance its capabilities. The concept is inspired by [ChatVector](https://arxiv.org/abs/2310.04799), aiming to modularize and transfer distinct skills across models.
+## SkillTree Details
+- **Functionality Status:** **Functional** / Non-Functional / Not Verified
+- **Base Model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+- **Skill Model:** [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
+- **Enhanced Model(optional):** [HachiML/Swallow-MS-7b-v0.1-ChatSkill](https://huggingface.co/HachiML/Swallow-MS-7b-v0.1-ChatSkill)
+- **Skill type:** Chat
 ## Uses
+### Limitation
+- **Model Architecture:** Mistral
+- **Model Size:** 7.24B
+- **Compatible Models[optional]:**
+### How to Apply Skill (Example)
+```python
+# Import library
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+# Load the target model to be applied skill
+base_model_name = "tokyotech-llm/Swallow-MS-7b-v0.1"  # Japanese Base Model
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_name,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+# Load SkillTree
+skill_tree = AutoModelForCausalLM.from_pretrained(
+    "HachiML/SkillTree-Chat-Mistral-7B-v0.1",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+)
+# Apply the skill to the target model
+def apply_skill(model, skill_tree):
+    # excluded object
+    skip_layers = ["model.embed_tokens.weight", "model.norm.weight", "lm_head.weight"]
+    # apply skill
+    for k, v in model.state_dict().items():
+        # layernorm is also excluded
+        if (k in skip_layers) or ("layernorm" in k):
+            continue
+        vector = skill_tree.state_dict()[k]
+        new_v = v + vector.to(v.device)
+        v.copy_(new_v)
+    return model
+model = apply_skill(model, skill_tree)
+# Push to hub
+model_name = "HachiML/Swallow-MS-7b-v0.1-ChatSkill"
+tokenizer.save_pretrained(f"./models/{model_name}", repo_id=model_name, push_to_hub=True)
+model.save_pretrained(f"./models/{model_name}", repo_id=model_name, push_to_hub=True)
+```