macadeliccc
/

CapyLake-7B-v2-laser

@@ -2,200 +2,55 @@
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 library_name: transformers
 tags: []
 ---
+# CapyLake-7B-v2-laser
+This model is a finetune of [cognitivecomputations/WestLake-7B-v2-Laser](https://huggingface.co/cognitivecomputations/WestLake-7B-v2-laser) on [argilla/distilabel-capybara-dpo-7k-binarized](https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized)
+![image/webp](https://cdn-uploads.huggingface.co/production/uploads/6455cc8d679315e4ef16fbec/kx2uwS_kZ-rTAJiusSrAW.webp)
+[<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-dark.png" alt="Built with Distilabel" width="200" height="32"/>](https://github.com/argilla-io/distilabel)
+## Process
++ Realigned the chat template to ChatML
++ Completed 1 Epoch
++ 5e-05 learning rate
++ Training time was about 2 hours on 1 H100
++ Cost was ~$8
+## Code Example
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "macadeliccc/CapyLake-7B-v2-laser"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+text = "Create an idea for a TV show and write a short pilot script"
+inputs = tokenizer(text, return_tensors="pt")
+# Adding hyperparameters to the generation call
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=4096,  # Controls the maximum length of the new tokens created
+    temperature=0.7,  # Adjust for creativity (lower is less random)
+    top_k=50,  # Keeps the top k tokens for sampling
+    top_p=0.95,  # Uses nucleus sampling with this cumulative probability
+    num_return_sequences=1,  # Number of sequences to generate
+    no_repeat_ngram_size=2,  # Prevents repeating n-grams to ensure diversity
+    early_stopping=True  # Stops generation when all sequences reach the EOS token
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+## Other Capy Models
+SOLAR-10.7B-Capy-v1.0 is also on the way. There could be more depending on performance!
+## Evaluations
+TODO