Add Devstral-Small-2505 FP8 quantized model and tokenizer

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
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+tekken.json filter=lfs diff=lfs merge=lfs -text
+

README.md

@@ -1,3 +1,214 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0 # Or the license of the original Devstral model if different
+tags:
+- mistral
+- devstral
+- fp8
+- quantization
+- llm-compressor
+- vllm
+pipeline_tag: text-generation
+---
+
+# Devstral-Small-2505-FP8-Dynamic
+
+This is a version of [mistralai/Devstral-Small-2505](https://huggingface.co/mistralai/Devstral-Small-2505) quantized to FP8 (weights and dynamic activations) using [llm-compressor](https://github.com/vllm-project/llm-compressor).
+
+This model format is particularly useful for accelerated inference with [vLLM](https://github.com/vllm-project/vllm) on NVIDIA GPUs with compute capability > 8.9 (Ada Lovelace, Hopper).
+
+## Model Description
+
+Devstral is a cutting-edge, versatile language model developed by Mistral AI, fine-tuned for development tasks. This version has been quantized to FP8 precision for weights (static, per-channel) and activations (dynamic, per-token), with the `lm_head` layer kept in its original precision.
+
+## Quantization with llm-compressor
+
+The model was quantized using the `oneshot` method from `llm-compressor` with the `FP8_DYNAMIC` scheme. 
+No calibration dataset was required for this quantization scheme.
+
+The key quantization configuration was:
+```python
+from llmcompressor.modifiers.quantization import QuantizationModifier
+
+recipe = QuantizationModifier(
+    targets="Linear", 
+    scheme="FP8_DYNAMIC", 
+    ignore=["lm_head"]
+)
+```
+
+The following script was used for conversion:
+```python
+import torch
+from transformers import AutoModelForCausalLM
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.protocol.instruct.messages import UserMessage
+from mistral_common.protocol.instruct.request import ChatCompletionRequest
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from huggingface_hub import hf_hub_download
+import shutil
+import os
+
+MODEL_ID = "mistralai/Devstral-Small-2505"
+SAVE_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic" # e.g., Devstral-Small-2505-FP8-Dynamic
+
+# Load model
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID, device_map="auto", torch_dtype="auto"
+)
+
+# Load tokenizer
+tekken_file_path = hf_hub_download(repo_id=MODEL_ID, filename="tekken.json")
+tokenizer = MistralTokenizer.from_file(tekken_file_path)
+
+# Configure quantization
+recipe = QuantizationModifier(
+    targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"]
+)
+
+# Apply quantization
+oneshot(model=model, tokenizer=tokenizer, recipe=recipe)
+
+# Save quantized model
+model.save_pretrained(SAVE_DIR)
+
+# Save tokenizer file
+if not os.path.exists(SAVE_DIR):
+    os.makedirs(SAVE_DIR)
+destination_tekken_file = os.path.join(SAVE_DIR, "tekken.json")
+shutil.copyfile(tekken_file_path, destination_tekken_file)
+
+print(f"Quantized model and tokenizer saved to {SAVE_DIR}")
+```
+
+## Inference Example
+This model can be loaded and run with transformers and mistral-common, or for optimized FP8 inference, with vLLM.
+
+### Using transformers and mistral-common (for functional checking, not FP8 optimized)
+
+```python
+import torch
+from transformers import AutoModelForCausalLM
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.protocol.instruct.messages import UserMessage, SystemMessage
+from mistral_common.protocol.instruct.request import ChatCompletionRequest
+from huggingface_hub import hf_hub_download
+
+MODEL_REPO_ID = "textgeflecht/Devstral-Small-2505-FP8-llmcompressor"
+
+# Load model
+# For FP8 inference, specific inference engines like vLLM are needed.
+# Transformers will load the weights but might not run them in true FP8.
+# device_map="auto" and torch_dtype="auto" are good starting points.
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_REPO_ID, 
+    device_map="auto", 
+    torch_dtype="auto" # or torch.bfloat16
+)
+
+# Load tokenizer from the tekken.json file within the repo
+tekken_file = hf_hub_download(repo_id=MODEL_REPO_ID, filename="tekken.json")
+tokenizer = MistralTokenizer.from_file(tekken_file)
+
+# (Optional) Load System Prompt if your model uses one and it's in the repo
+# try:
+#     system_prompt_file = hf_hub_download(repo_id=MODEL_REPO_ID, filename="SYSTEM_PROMPT.txt")
+#     with open(system_prompt_file, "r") as f:
+#         SYSTEM_PROMPT = f.read()
+# except Exception: # pylint: disable=broad-except
+#     SYSTEM_PROMPT = "You are a helpful coding assistant." # Default or example
+
+prompt = "Write a python function that calculates the factorial of a number."
+messages = [
+    # SystemMessage(content=SYSTEM_PROMPT), # Uncomment if using a system prompt
+    UserMessage(content=prompt)
+]
+
+# Create ChatCompletionRequest
+chat_request = ChatCompletionRequest(messages=messages)
+
+# Encode the request
+tokenized_payload = tokenizer.encode_chat_completion(chat_request)
+input_ids = torch.tensor([tokenized_payload.tokens], device=model.device)
+attention_mask = torch.ones_like(input_ids)
+
+# Generate output
+# Note: Setting pad_token_id to eos_token_id is common for open-ended generation
+output = model.generate(
+    input_ids, 
+    attention_mask=attention_mask,
+    max_new_tokens=200, 
+    pad_token_id=tokenizer.eos_id,
+    do_sample=True, # Add sampling parameters for more diverse outputs
+    top_p=0.9,
+    temperature=0.7
+)
+
+# Decode only the generated tokens
+generated_tokens = output[0][len(tokenized_payload.tokens):]
+decoded_output = tokenizer.decode(generated_tokens.tolist())
+
+print("Original Prompt:\n", prompt)
+print("\nGenerated Output:\n", decoded_output)
+```
+
+### Using vLLM (for optimized FP8 inference)
+Ensure you have vllm installed (pip install vllm). 
+This requires a compatible GPU (NVIDIA Ada/Hopper/Blackwell or newer architecture).
+
+```python
+from vllm import LLM, SamplingParams
+
+# Define your model ID
+MODEL_REPO_ID = "textgeflecht/Devstral-Small-2505-FP8-llmcompressor"
+
+# Initialize vLLM with the model
+# vLLM should automatically detect the FP8 quantization if the format is compatible.
+# The 'quantization' parameter might be needed if auto-detection fails,
+# often models from llm-compressor are saved in 'compressed-tensors' format
+# which vLLM aims to support. You might need to specify tokenizer_mode='custom'
+# and point to the tekken.json or use a HuggingFace compatible tokenizer wrapper if needed.
+
+# For MistralTokenizer, vLLM might require manual handling or a specific setup.
+# If vLLM doesn't directly support MistralTokenizer from tekken.json,
+# you might need to use a HF AutoTokenizer equivalent IF one can be made compatible,
+# or wait for broader custom tokenizer support in vLLM.
+
+# Simplest case (assuming vLLM handles the compressed-tensors format and tokenizer):
+llm = LLM(model=MODEL_REPO_ID, trust_remote_code=True) 
+# If tekken.json is present, vLLM might use it if it knows the tokenizer type.
+# Or, if a compatible tokenizer_config.json can be created for MistralTokenizer.
+
+# For now, a more robust way with vLLM and custom tokenizers like Mistral's
+# might involve more direct specification or ensuring the saved format is fully
+# vLLM auto-detectable. If issues arise, consulting vLLM documentation for custom/FP8 models is key.
+
+# --- Placeholder for vLLM with MistralTokenizer ---
+# Direct vLLM usage with MistralTokenizer from a tekken.json might be tricky
+# as vLLM typically relies on Hugging Face AutoTokenizer.
+# One approach could be to use the HF AutoModelForCausalLM loading mechanism
+# and then pass it to vLLM if supported, or ensure the saved format is directly consumable.
+
+# Let's assume for now a standard prompt for demonstration if vLLM picks up a compatible tokenizer.
+# If not, you'd use MistralTokenizer to prepare the prompt as in the transformers example.
+
+prompts = [
+    "[INST] Write a python function that calculates the factorial of a number. [/INST]",
+    # Add more prompts if needed
+]
+
+sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=200)
+
+outputs = llm.generate(prompts, sampling_params)
+
+# Print the outputs
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}")
+    print(f"Generated text: {generated_text!r}\n---")
+```
+
+Original Model Card (mistralai/Devstral-Small-2505)
+For more details on the base model, please refer to the original model card: https://huggingface.co/mistralai/Devstral-Small-2505
+

config.json

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+{
+  "_name_or_path": "mistralai/Devstral-Small-2505",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 32768,
+  "max_position_embeddings": 131072,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 8,
+  "quantization_config": {
+    "config_groups": {
+      "group_0": {
+        "input_activations": {
+          "actorder": null,
+          "block_structure": null,
+          "dynamic": true,
+          "group_size": null,
+          "num_bits": 8,
+          "observer": null,
+          "observer_kwargs": {},
+          "strategy": "token",
+          "symmetric": true,
+          "type": "float"
+        },
+        "output_activations": null,
+        "targets": [
+          "Linear"
+        ],
+        "weights": {
+          "actorder": null,
+          "block_structure": null,
+          "dynamic": false,
+          "group_size": null,
+          "num_bits": 8,
+          "observer": "minmax",
+          "observer_kwargs": {},
+          "strategy": "channel",
+          "symmetric": true,
+          "type": "float"
+        }
+      }
+    },
+    "format": "float-quantized",
+    "global_compression_ratio": null,
+    "ignore": [
+      "lm_head"
+    ],
+    "kv_cache_scheme": null,
+    "quant_method": "compressed-tensors",
+    "quantization_status": "compressed"
+  },
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.48.3",
+  "use_cache": true,
+  "vocab_size": 131072
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.48.3"
+}

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recipe.yaml

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+default_stage:
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+      ignore: [lm_head]
+      targets: [Linear]
+      scheme: FP8_DYNAMIC

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