1111

README.md

@@ -1,72 +1,17 @@
 ---
-library_name: vllm
-language:
-- code
-license: other
-tags:
-- code
-- mistral-common
 inference: false
+license: other
 license_name: mnpl
 license_link: https://mistral.ai/licences/MNPL-0.1.md
-
-extra_gated_description: If you want to learn more about how we process your personal data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
+tags:
+- code
+language:
+- code
 ---
 
 # Model Card for Codestral-22B-v0.1
 
-
-## Encode and Decode with `mistral_common`
-            
-```py
-from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
-from mistral_common.protocol.instruct.messages import UserMessage
-from mistral_common.protocol.instruct.request import ChatCompletionRequest
- 
-mistral_models_path = "MISTRAL_MODELS_PATH"
- 
-tokenizer = MistralTokenizer.v3()
- 
-completion_request = ChatCompletionRequest(messages=[UserMessage(content="Explain Machine Learning to me in a nutshell.")])
- 
-tokens = tokenizer.encode_chat_completion(completion_request).tokens
-```
- 
-## Inference with `mistral_inference`
- 
- ```py
-from mistral_inference.transformer import Transformer
-from mistral_inference.generate import generate
- 
-model = Transformer.from_folder(mistral_models_path)
-out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
-
-result = tokenizer.decode(out_tokens[0])
-
-print(result)
-```
-
-## Inference with hugging face `transformers`
- 
-```py
-from transformers import AutoModelForCausalLM
- 
-model = AutoModelForCausalLM.from_pretrained("mistralai/Codestral-22B-v0.1")
-model.to("cuda")
- 
-generated_ids = model.generate(tokens, max_new_tokens=1000, do_sample=True)
-
-# decode with mistral tokenizer
-result = tokenizer.decode(generated_ids[0].tolist())
-print(result)
-```
-
-> [!TIP]
-> PRs to correct the `transformers` tokenizer so that it gives 1-to-1 the same results as the `mistral_common` reference implementation are very welcome!
-            
----
-
-Codestral-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried:
+Codestrall-22B-v0.1 is trained on a diverse dataset of 80+ programming languages, including the most popular ones, such as Python, Java, C, C++, JavaScript, and Bash (more details in the [Blogpost](https://mistral.ai/news/codestral/)). The model can be queried:
 - As instruct, for instance to answer any questions about a code snippet (write documentation, explain, factorize) or to generate code following specific indications
 - As Fill in the Middle (FIM), to predict the middle tokens between a prefix and a suffix (very useful for software development add-ons like in VS Code)
 
@@ -126,7 +71,7 @@ This function uses recursion to calculate the Fibonacci number. However, it's no
 After installing `mistral_inference` and running `pip install --upgrade mistral_common` to make sure to have mistral_common>=1.2 installed:
 
 ```py
-from mistral_inference.transformer import Transformer
+from mistral_inference.model import Transformer
 from mistral_inference.generate import generate
 from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
 from mistral_common.tokens.instruct.request import FIMRequest
@@ -159,27 +104,6 @@ num1, num2):
     # return the sum
 ```
 
-## Usage with transformers library
-
-This model is also compatible with `transformers` library, first run `pip install -U transformers` then use the snippet below to quickly get started:
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_id = "mistralai/Codestral-22B-v0.1"
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-
-model = AutoModelForCausalLM.from_pretrained(model_id)
-
-text = "Hello my name is"
-inputs = tokenizer(text, return_tensors="pt")
-
-outputs = model.generate(**inputs, max_new_tokens=20)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
-
-By default, transformers will load the model in full precision. Therefore you might be interested to further reduce down the memory requirements to run the model through the optimizations we offer in HF ecosystem.
-
 ## Limitations
 
 The Codestral-22B-v0.1 does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to

config.json

@@ -1,26 +0,0 @@
-{
-  "_name_or_path": "mistral-community/Codestral-22B-v0.1",
-  "architectures": [
-    "MistralForCausalLM"
-  ],
-  "attention_dropout": 0.0,
-  "bos_token_id": 1,
-  "eos_token_id": 2,
-  "hidden_act": "silu",
-  "hidden_size": 6144,
-  "initializer_range": 0.02,
-  "intermediate_size": 16384,
-  "max_position_embeddings": 32768,
-  "model_type": "mistral",
-  "num_attention_heads": 48,
-  "num_hidden_layers": 56,
-  "num_key_value_heads": 8,
-  "rms_norm_eps": 1e-05,
-  "rope_theta": 1000000.0,
-  "sliding_window": null,
-  "tie_word_embeddings": false,
-  "torch_dtype": "bfloat16",
-  "transformers_version": "4.42.0.dev0",
-  "use_cache": true,
-  "vocab_size": 32768
-}

generation_config.json

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-{
-  "_from_model_config": true,
-  "bos_token_id": 1,
-  "eos_token_id": 2,
-  "transformers_version": "4.42.0.dev0"
-}

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special_tokens_map.json

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