prince-canuma
/

c4ai-command-r-v01-4bit

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 library_name: transformers
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 ---
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 ---
 library_name: transformers
+language:
+- en
+- fr
+- de
+- es
+- it
+- pt
+- ja
+- ko
+- zh
+- ar
 ---
+# Model Card for C4AI Command-R quantized to 4bit
+## Model Summary
+This repo contains a 4bit quantized version of C4AI Command-R.
+A 35 billion parameter highly performant generative model. Command-R is a large language model with open weights optimized for a variety of use cases including reasoning, summarization, and question answering. Command-R has the capability for multilingual generation evaluated in 10 languages and highly performant RAG capabilities.
+Developed by: Cohere and [Cohere For AI](https://cohere.for.ai)
+- Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/)
+- License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy)
+- Model: c4ai-command-r-v01
+- Model Size: 35 billion parameters
+- Context length: 128K
+**Use**
+```python
+# pip install transformers
+from transformers import AutoTokenizer, AutoModelForCausalLM
+model_id = "prince-canuma/c4ai-command-r-v01-4bit"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
+# Format message with the command-r chat template
+messages = [{"role": "user", "content": "Hello, how are you?"}]
+input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
+## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>
+gen_tokens = model.generate(
+    input_ids,
+    max_new_tokens=100,
+    do_sample=True,
+    temperature=0.3,
+    )
+gen_text = tokenizer.decode(gen_tokens[0])
+print(gen_text)
+```
+## Model Details
+**Input**: Models input text only.
+**Output**: Models generate text only.
+**Model Architecture**: This is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model uses supervised fine-tuning (SFT) and preference training to align model behavior to human preferences for helpfulness and safety.
+**Languages covered**: The model is optimized to perform well in the following languages: English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Simplified Chinese, and Arabic.
+Pre-training data additionally included the following 13 languages: Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, Persian.
+**Context length**: Command-R supports a context length of 128K.
+### Code Capabilities:
+Command-R has been optimized to interact with your code, by requesting code snippets, code explanations, or code rewrites. It might not perform well out-of-the-box for pure code completion. For better performance, we also recommend using a low temperature (and even greedy decoding) for code-generation related instructions.
+### Model Card Contact
+For errors or additional questions about details in this model card, contact [info@for.ai](mailto:info@for.ai).
+### Terms of Use:
+We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 35 billion parameter model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy).
+### Try Chat:
+You can try Command-R chat in the playground [here](https://dashboard.cohere.com/playground/chat).