Upload folder using huggingface_hub

README.md

@@ -1,5 +1,4 @@
 ---
-license: apache-2.0
 base_model:
 - openlm-research/open_llama_7b
 - stabilityai/StableBeluga-7B
@@ -7,38 +6,17 @@ tags:
 - merge
 - mergekit
 - lazymergekit
-- open_llama
-- StableBeluga
-- slerp
+- openlm-research/open_llama_7b
+- stabilityai/StableBeluga-7B
 ---
 
 # OpenLlama-Stable-7B
 
-This is a merge of pre-trained language models created using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing), combining the foundational capabilities of OpenLM's Open Llama with StabilityAI's StableBeluga through an efficient SLERP fusion.
-
-## About Me
-
-I'm David Soeiro-Vuong, a third-year Computer Science student working as an apprentice at TW3 Partners, a company specialized in Generative AI. Passionate about artificial intelligence and language models optimization, I focus on creating efficient model merges that balance performance and capabilities.
-
-🔗 [Connect with me on LinkedIn](https://www.linkedin.com/in/david-soeiro-vuong-a28b582ba/)
-
-## Merge Details
-
-### Merge Method
+OpenLlama-Stable-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
+* [openlm-research/open_llama_7b](https://huggingface.co/openlm-research/open_llama_7b)
+* [stabilityai/StableBeluga-7B](https://huggingface.co/stabilityai/StableBeluga-7B)
 
-This model uses SLERP (Spherical Linear Interpolation) with carefully tuned parameters to achieve optimal performance balance:
-
-- **Attention Layers**: 0.7 interpolation value favoring StableBeluga's strong instruction-following capabilities
-- **MLP Layers**: 0.5 interpolation value creating an equal blend for balanced reasoning
-- **Other Parameters**: 0.6 interpolation value slightly favoring StableBeluga's refinements
-- **Format**: bfloat16 precision for efficient memory usage
-
-### Models Merged
-
-* [openlm-research/open_llama_7b](https://huggingface.co/openlm-research/open_llama_7b) - An open-source reproduction of Meta's LLaMA that offers strong base capabilities
-* [stabilityai/StableBeluga-7B](https://huggingface.co/stabilityai/StableBeluga-7B) - StabilityAI's instruction-tuned variant offering improved instruction following and coherence
-
-### Configuration
+## 🧩 Configuration
 
 ```yaml
 slices:
@@ -62,57 +40,27 @@ parameters:
 dtype: bfloat16
 ```
 
-## Model Capabilities
-
-This merge combines:
-- Open Llama's strong foundational knowledge and reasoning
-- StableBeluga's improved instruction following and coherence
-- Fully open architecture with no usage restrictions
-
-The resulting model provides enhanced performance on tasks requiring both strong reasoning and good instruction following, such as:
-- Detailed explanations of complex concepts
-- Creative writing with coherent structure
-- Problem-solving with step-by-step reasoning
-- Balanced factual responses with nuanced perspectives
-
-## Usage
+## 💻 Usage
 
 ```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-
-model_id = "david-sv/OpenLlama-Stable-7B"  # Replace with your actual HF username
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(
-    model_id,
-    torch_dtype=torch.float16,
-    device_map="auto"
-)
+!pip install -qU transformers accelerate
 
-# For chat completions
-prompt = """<human>: Explain the concept of spherical linear interpolation (SLERP) and why it's useful for merging language models.
+from transformers import AutoTokenizer
+import transformers
+import torch
 
-<assistant>:"""
+model = "Davidsv/OpenLlama-Stable-7B"
+messages = [{"role": "user", "content": "What is a large language model?"}]
 
-inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-output = model.generate(
-    inputs["input_ids"],
-    max_new_tokens=512,
-    temperature=0.7,
-    top_p=0.9,
-    repetition_penalty=1.1
+tokenizer = AutoTokenizer.from_pretrained(model)
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    torch_dtype=torch.float16,
+    device_map="auto",
 )
 
-print(tokenizer.decode(output[0], skip_special_tokens=True))
-```
-
-## Limitations
-
-- Inherits limitations from both base models
-- May exhibit inconsistent behavior for certain complex reasoning tasks
-- No additional alignment or fine-tuning beyond the base models' training
-- Model was created through parameter merging without additional training data
-
-## License
-
-This model is released under the Apache 2.0 license, consistent with the underlying models' licenses.
+outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])
+```