Quant for 8.0

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tekken.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -10,69 +10,211 @@ language:
   - ru
   - zh
   - ja
-quantized_by: bartowski
-pipeline_tag: text-generation
 ---
 
-## Exllama v2 Quantizations of Mistral-Nemo-Instruct-2407
+# Model Card for Mistral-Nemo-Instruct-2407
 
-Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.1.7">turboderp's ExLlamaV2 v0.1.7</a> for quantization.
+The Mistral-Nemo-Instruct-2407 Large Language Model (LLM) is an instruct fine-tuned version of the [Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407). Trained jointly by Mistral AI and NVIDIA, it significantly outperforms existing models smaller or similar in size.
 
-<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
+For more details about this model please refer to our release [blog post](https://mistral.ai/news/mistral-nemo/).
 
-Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
+## Key features
+- Released under the **Apache 2 License**
+- Pre-trained and instructed versions
+- Trained with a **128k context window**
+- Trained on a large proportion of **multilingual and code data**
+- Drop-in replacement of Mistral 7B
 
-Conversion was done using the default calibration dataset.
+## Model Architecture
+Mistral Nemo is a transformer model, with the following architecture choices:
+- **Layers:** 40
+- **Dim:** 5,120
+- **Head dim:** 128
+- **Hidden dim:** 14,436
+- **Activation Function:** SwiGLU
+- **Number of heads:** 32
+- **Number of kv-heads:** 8 (GQA)
+- **Vocabulary size:** 2**17 ~= 128k
+- **Rotary embeddings (theta = 1M)**
 
-Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
+## Metrics
 
-Original model: https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407
+### Main Benchmarks
 
+| Benchmark | Score |
+| --- | --- |
+| HellaSwag (0-shot) | 83.5% |
+| Winogrande (0-shot) | 76.8% |
+| OpenBookQA (0-shot) | 60.6% |
+| CommonSenseQA (0-shot) | 70.4% |
+| TruthfulQA (0-shot) | 50.3% |
+| MMLU (5-shot) | 68.0% |
+| TriviaQA (5-shot) | 73.8% |
+| NaturalQuestions (5-shot) | 31.2% |
 
-<a href="https://huggingface.co/bartowski/Mistral-Nemo-Instruct-2407-exl2/tree/8_0">8.0 bits per weight</a>
+### Multilingual Benchmarks (MMLU)
 
-<a href="https://huggingface.co/bartowski/Mistral-Nemo-Instruct-2407-exl2/tree/6_5">6.5 bits per weight</a>
+| Language | Score |
+| --- | --- |
+| French | 62.3% |
+| German | 62.7% |
+| Spanish | 64.6% |
+| Italian | 61.3% |
+| Portuguese | 63.3% |
+| Russian | 59.2% |
+| Chinese | 59.0% |
+| Japanese | 59.0% |
 
-<a href="https://huggingface.co/bartowski/Mistral-Nemo-Instruct-2407-exl2/tree/5_0">5.0 bits per weight</a>
+## Usage
 
-<a href="https://huggingface.co/bartowski/Mistral-Nemo-Instruct-2407-exl2/tree/4_25">4.25 bits per weight</a>
+The model can be used with three different frameworks
 
-<a href="https://huggingface.co/bartowski/Mistral-Nemo-Instruct-2407-exl2/tree/3_5">3.5 bits per weight</a>
+- [`mistral_inference`](https://github.com/mistralai/mistral-inference): See [here](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407#mistral-inference)
+- [`transformers`](https://github.com/huggingface/transformers): See [here](#transformers)
+- [`NeMo`](https://github.com/NVIDIA/NeMo): See [nvidia/Mistral-NeMo-12B-Instruct](https://huggingface.co/nvidia/Mistral-NeMo-12B-Instruct)
 
+### Mistral Inference
 
-## Download instructions
+#### Install
 
-With git:
+It is recommended to use `mistralai/Mistral-Nemo-Instruct-2407` with [mistral-inference](https://github.com/mistralai/mistral-inference). For HF transformers code snippets, please keep scrolling.
 
-```shell
-git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Mistral-Nemo-Instruct-2407-exl2
 ```
+pip install mistral_inference
+```
+
+#### Download
+
+```py
+from huggingface_hub import snapshot_download
+from pathlib import Path
 
-With huggingface hub (credit to TheBloke for instructions):
+mistral_models_path = Path.home().joinpath('mistral_models', 'Nemo-Instruct')
+mistral_models_path.mkdir(parents=True, exist_ok=True)
 
-```shell
-pip3 install huggingface-hub
+snapshot_download(repo_id="mistralai/Mistral-Nemo-Instruct-2407", allow_patterns=["params.json", "consolidated.safetensors", "tekken.json"], local_dir=mistral_models_path)
 ```
 
-To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Mistral-Nemo-Instruct-2407-exl2`:
+#### Chat
+
+After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. You can chat with the model using
+
+```
+mistral-chat $HOME/mistral_models/Nemo-Instruct --instruct --max_tokens 256 --temperature 0.35
+```
 
-```shell
-mkdir Mistral-Nemo-Instruct-2407-exl2
-huggingface-cli download bartowski/Mistral-Nemo-Instruct-2407-exl2 --local-dir Mistral-Nemo-Instruct-2407-exl2
+*E.g.* Try out something like:
+```
+How expensive would it be to ask a window cleaner to clean all windows in Paris. Make a reasonable guess in US Dollar.
 ```
 
-To download from a different branch, add the `--revision` parameter:
+#### Instruct following
+
+```py
+from mistral_inference.transformer import Transformer
+from mistral_inference.generate import generate
+
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.protocol.instruct.messages import UserMessage
+from mistral_common.protocol.instruct.request import ChatCompletionRequest
+
+tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")
+model = Transformer.from_folder(mistral_models_path)
+
+prompt = "How expensive would it be to ask a window cleaner to clean all windows in Paris. Make a reasonable guess in US Dollar."
 
-Linux:
+completion_request = ChatCompletionRequest(messages=[UserMessage(content=prompt)])
 
-```shell
-mkdir Mistral-Nemo-Instruct-2407-exl2-6_5
-huggingface-cli download bartowski/Mistral-Nemo-Instruct-2407-exl2 --revision 6_5 --local-dir Mistral-Nemo-Instruct-2407-exl2-6_5
+tokens = tokenizer.encode_chat_completion(completion_request).tokens
+
+out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.35, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
+result = tokenizer.decode(out_tokens[0])
+
+print(result)
+```
+
+#### Function calling
+
+```py
+from mistral_common.protocol.instruct.tool_calls import Function, Tool
+from mistral_inference.transformer import Transformer
+from mistral_inference.generate import generate
+
+from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
+from mistral_common.protocol.instruct.messages import UserMessage
+from mistral_common.protocol.instruct.request import ChatCompletionRequest
+
+
+tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tekken.json")
+model = Transformer.from_folder(mistral_models_path)
+
+completion_request = ChatCompletionRequest(
+    tools=[
+        Tool(
+            function=Function(
+                name="get_current_weather",
+                description="Get the current weather",
+                parameters={
+                    "type": "object",
+                    "properties": {
+                        "location": {
+                            "type": "string",
+                            "description": "The city and state, e.g. San Francisco, CA",
+                        },
+                        "format": {
+                            "type": "string",
+                            "enum": ["celsius", "fahrenheit"],
+                            "description": "The temperature unit to use. Infer this from the users location.",
+                        },
+                    },
+                    "required": ["location", "format"],
+                },
+            )
+        )
+    ],
+    messages=[
+        UserMessage(content="What's the weather like today in Paris?"),
+        ],
+)
+
+tokens = tokenizer.encode_chat_completion(completion_request).tokens
+
+out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.35, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
+result = tokenizer.decode(out_tokens[0])
+
+print(result)
 ```
 
-Windows (which apparently doesn't like _ in folders sometimes?):
+### Transformers
+
+> [!IMPORTANT]
+> NOTE: Until a new release has been made, you need to install transformers from source:
+> ```sh
+> pip install git+https://github.com/huggingface/transformers.git
+> ```
+
+If you want to use Hugging Face `transformers` to generate text, you can do something like this.
 
-```shell
-mkdir Mistral-Nemo-Instruct-2407-exl2-6.5
-huggingface-cli download bartowski/Mistral-Nemo-Instruct-2407-exl2 --revision 6_5 --local-dir Mistral-Nemo-Instruct-2407-exl2-6.5
+```py
+from transformers import pipeline
+
+messages = [
+    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
+    {"role": "user", "content": "Who are you?"},
+]
+chatbot = pipeline("text-generation", model="mistralai/Mistral-Nemo-Instruct-2407")
+chatbot(messages)
 ```
+
+> [!TIP]
+> Unlike previous Mistral models, Mistral Nemo requires smaller temperatures. We recommend to use a temperature of 0.3.
+
+## Limitations
+
+The Mistral Nemo Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. 
+It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
+make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
+
+## The Mistral AI Team
+
+Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Alok Kothari, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Augustin Garreau, Austin Birky, Bam4d, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Carole Rambaud, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gaspard Blanchet, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Hichem Sattouf, Ian Mack, Jean-Malo Delignon, Jessica Chudnovsky, Justus Murke, Kartik Khandelwal, Lawrence Stewart, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Marjorie Janiewicz, Mickaël Seznec, Nicolas Schuhl, Niklas Muhs, Olivier de Garrigues, Patrick von Platen, Paul Jacob, Pauline Buche, Pavan Kumar Reddy, Perry Savas, Pierre Stock, Romain Sauvestre, Sagar Vaze, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibault Schueller, Thibaut Lavril, Thomas Wang, Théophile Gervet, Timothée Lacroix, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall

config.json

@@ -0,0 +1,37 @@
+{
+    "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": 14336,
+    "max_position_embeddings": 1024000,
+    "model_type": "mistral",
+    "num_attention_heads": 32,
+    "num_hidden_layers": 40,
+    "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": "4.43.0.dev0",
+    "use_cache": true,
+    "vocab_size": 131072,
+    "quantization_config": {
+        "quant_method": "exl2",
+        "version": "0.1.7",
+        "bits": 8.0,
+        "head_bits": 8,
+        "calibration": {
+            "rows": 115,
+            "length": 2048,
+            "dataset": "(default)"
+        }
+    }
+}

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.43.0.dev0"
+}

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