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README.md

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+---
+inference: false
+license: other
+license_name: mnpl
+license_link: https://mistral.ai/licences/MNPL-0.1.md
+tags:
+- code
+language:
+- code
+---
+**Exllamav2** quant (**exl2** / **8.0 bpw**) made with ExLlamaV2 v0.1.1
+
+Other EXL2 quants:
+| **Quant** | **Model Size** | **lm_head** |
+| ----- | ---------- | ------- |
+|<center>**[2.2](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_2bpw_exl2)**</center> | <center>6296 MB</center> | <center>6</center> |
+|<center>**[2.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-2_5bpw_exl2)**</center> | <center>7045 MB</center> | <center>6</center> |
+|<center>**[3.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_0bpw_exl2)**</center> | <center>8347 MB</center> | <center>6</center> |
+|<center>**[3.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_5bpw_exl2)**</center> | <center>9652 MB</center> | <center>6</center> |
+|<center>**[3.75](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-3_75bpw_exl2)**</center> | <center>10297 MB</center> | <center>6</center> |
+|<center>**[4.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_0bpw_exl2)**</center> | <center>10953 MB</center> | <center>6</center> |
+|<center>**[4.25](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-4_25bpw_exl2)**</center> | <center>11603 MB</center> | <center>6</center> |
+|<center>**[5.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-5_0bpw_exl2)**</center> | <center>13553 MB</center> | <center>6</center> |
+|<center>**[6.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_0bpw_exl2)**</center> | <center>16185 MB</center> | <center>8</center> |
+|<center>**[6.5](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-6_5bpw_exl2)**</center> | <center>17484 MB</center> | <center>8</center> |
+|<center>**[8.0](https://huggingface.co/Zoyd/bullerwins_Codestral-22B-v0.1-hf-8_0bpw_exl2)**</center> | <center>19350 MB</center> | <center>8</center> |
+
+
+Converted using [this](https://huggingface.co/bullerwins/Codestral-22B-v0.1-hf/blob/main/convert_mistral_weights_to_hf-22B.py) script
+
+# Model Card for Codestral-22B-v0.1
+
+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)
+
+
+## Installation
+
+It is recommended to use `mistralai/Codestral-22B-v0.1` with [mistral-inference](https://github.com/mistralai/mistral-inference).
+
+```
+pip install mistral_inference
+```
+
+## Download
+
+```py
+from huggingface_hub import snapshot_download
+from pathlib import Path
+
+mistral_models_path = Path.home().joinpath('mistral_models', 'Codestral-22B-v0.1')
+mistral_models_path.mkdir(parents=True, exist_ok=True)
+
+snapshot_download(repo_id="mistralai/Codestral-22B-v0.1", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)
+```
+
+### Chat
+
+After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment.
+
+```
+mistral-chat $HOME/mistral_models/Codestral-22B-v0.1 --instruct --max_tokens 256
+```
+
+Will generate an answer to "Write me a function that computes fibonacci in Rust" and should give something along the following lines:
+
+```
+Sure, here's a simple implementation of a function that computes the Fibonacci sequence in Rust. This function takes an integer `n` as an argument and returns the `n`th Fibonacci number.
+
+fn fibonacci(n: u32) -> u32 {
+    match n {
+        0 => 0,
+        1 => 1,
+        _ => fibonacci(n - 1) + fibonacci(n - 2),
+    }
+}
+
+fn main() {
+    let n = 10;
+    println!("The {}th Fibonacci number is: {}", n, fibonacci(n));
+}
+
+This function uses recursion to calculate the Fibonacci number. However, it's not the most efficient solution because it performs a lot of redundant calculations. A more efficient solution would use a loop to iteratively calculate the Fibonacci numbers.
+```
+
+
+### Fill-in-the-middle (FIM)
+
+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.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
+
+tokenizer = MistralTokenizer.v3()
+model = Transformer.from_folder("~/codestral-22B-240529")
+
+prefix = """def add("""
+suffix = """    return sum"""
+
+request = FIMRequest(prompt=prefix, suffix=suffix)
+
+tokens = tokenizer.encode_fim(request).tokens
+
+out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
+result = tokenizer.decode(out_tokens[0])
+
+middle = result.split(suffix)[0].strip()
+print(middle)
+```
+
+Should give something along the following lines:
+
+```
+num1, num2):
+
+    # Add two numbers
+    sum = num1 + num2
+
+    # return the sum
+```
+
+## Limitations
+
+The Codestral-22B-v0.1 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.
+
+## License
+
+Codestral-22B-v0.1 is released under the `MNLP-0.1` license.
+
+## The Mistral AI Team
+
+Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Henri Roussez, Jean-Malo Delignon, Jia Li, 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, Mickael Seznec, Nicolas Schuhl, Patrick von Platen, Romain Sauvestre, Pierre Stock, Sandeep Subramanian, Saurabh Garg, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Thibault Schueller, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, Wendy Shang, William El Sayed, William Marshall

config.json

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+{
+    "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": "4.40.2",
+    "use_cache": true,
+    "vocab_size": 32768,
+    "quantization_config": {
+        "quant_method": "exl2",
+        "version": "0.1.1",
+        "bits": 8.0,
+        "head_bits": 8,
+        "calibration": {
+            "rows": 100,
+            "length": 2048,
+            "dataset": "(default)"
+        }
+    }
+}

convert_mistral_weights_to_hf-22B.py

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+# Copyright 2023 Mistral AI and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import argparse
+import gc
+import json
+import os
+import shutil
+import warnings
+
+import torch
+from safetensors.torch import load_file as safe_load_file
+
+from transformers import (
+    LlamaTokenizer,
+    MistralConfig,
+    MistralForCausalLM,
+)
+
+
+try:
+    from transformers import LlamaTokenizerFast
+
+    tokenizer_class = LlamaTokenizerFast
+except ImportError as e:
+    warnings.warn(e)
+    warnings.warn(
+        "The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"
+    )
+    tokenizer_class = LlamaTokenizer
+
+"""
+Sample usage:
+
+```
+python src/transformers/models/mistral/convert_mistral_weights_to_hf.py \
+    --input_dir /path/to/downloaded/mistral/weights --model_size 22B --output_dir /output/path
+```
+
+Thereafter, models can be loaded via:
+
+```py
+from transformers import MistralForCausalLM, LlamaTokenizer
+
+model = MistralForCausalLM.from_pretrained("/output/path")
+tokenizer = LlamaTokenizer.from_pretrained("/output/path")
+```
+
+Important note: you need to be able to host the whole model in RAM to execute this script (even if the biggest versions
+come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM).
+"""
+
+NUM_SHARDS = {"22B": 1}
+
+
+def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256):
+    return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)
+
+
+def read_json(path):
+    with open(path, "r") as f:
+        return json.load(f)
+
+
+def write_json(text, path):
+    with open(path, "w") as f:
+        json.dump(text, f)
+
+
+def write_model(model_path, input_base_path, model_size, tokenizer_path=None, safe_serialization=True, is_v3=False):
+    # for backward compatibility, before you needed the repo to be called `my_repo/model_size`
+    if not os.path.isfile(os.path.join(input_base_path, "params.json")):
+        input_base_path = os.path.join(input_base_path, model_size)
+
+    os.makedirs(model_path, exist_ok=True)
+    tmp_model_path = os.path.join(model_path, "tmp")
+    os.makedirs(tmp_model_path, exist_ok=True)
+
+    params = read_json(os.path.join(input_base_path, "params.json"))
+    num_shards = NUM_SHARDS[model_size]
+
+    sliding_window = params.get("sliding_window", None)
+
+    # For some reason this is a string in the params.json
+    if sliding_window is not None:
+        sliding_window = int(sliding_window)
+
+    n_layers = params["n_layers"]
+    n_heads = params["n_heads"]
+    n_heads_per_shard = n_heads // num_shards
+    dim = params["dim"]
+    dims_per_head = dim // n_heads
+    base = params.get("rope_theta", 10000.0)
+    inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
+    max_position_embeddings = 4096 * 8
+
+    if tokenizer_path is not None:
+        tokenizer = tokenizer_class(tokenizer_path + ".v3" if is_v3 else "")
+        tokenizer.save_pretrained(model_path)
+    vocab_size = tokenizer.vocab_size if tokenizer_path is not None else 32000
+
+    if "n_kv_heads" in params:
+        num_key_value_heads = params["n_kv_heads"]  # for GQA / MQA
+        num_local_key_value_heads = num_key_value_heads // num_shards
+        key_value_dim = dims_per_head * num_local_key_value_heads
+    else:  # compatibility with other checkpoints
+        num_key_value_heads = n_heads
+        num_local_key_value_heads = n_heads_per_shard
+        key_value_dim = dim
+
+    # permute for sliced rotary
+    def permute(w, n_heads=n_heads, dim1=dim, dim2=dim):
+        return w.view(n_heads, dim1 // n_heads // 2, 2, dim2).transpose(1, 2).reshape(dim1, dim2)
+
+    print(f"Fetching all parameters from the checkpoint at {input_base_path}.")
+
+    # Load weights - for v3 models the consolidated weights are in a single file format in safetensors
+    if is_v3:
+        loaded = [safe_load_file(os.path.join(input_base_path, "consolidated.safetensors"))]
+    else:
+        loaded = [
+            torch.load(os.path.join(input_base_path, f"consolidated.{i:02d}.pth"), map_location="cpu")
+            for i in range(num_shards)
+        ]
+    param_count = 0
+    index_dict = {"weight_map": {}}
+    for layer_i in range(n_layers):
+        filename = f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
+
+        # Sharded
+        # Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
+        # the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
+        # redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.
+
+        state_dict = {
+            f"model.layers.{layer_i}.input_layernorm.weight": loaded[0][
+                f"layers.{layer_i}.attention_norm.weight"
+            ].clone(),
+            f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[0][
+                f"layers.{layer_i}.ffn_norm.weight"
+            ].clone(),
+        }
+        state_dict[f"model.layers.{layer_i}.self_attn.q_proj.weight"] = permute(
+            torch.cat(
+                [
+                    loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(n_heads_per_shard, dims_per_head, dim)
+                    for i in range(num_shards)
+                ],
+                dim=0,
+            ).reshape(dim, dim)
+        )
+        state_dict[f"model.layers.{layer_i}.self_attn.k_proj.weight"] = permute(
+            torch.cat(
+                [
+                    loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(
+                        num_local_key_value_heads, dims_per_head, dim
+                    )
+                    for i in range(num_shards)
+                ],
+                dim=0,
+            ).reshape(key_value_dim, dim),
+            num_key_value_heads,
+            key_value_dim,
+            dim,
+        )
+        state_dict[f"model.layers.{layer_i}.self_attn.v_proj.weight"] = torch.cat(
+            [
+                loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(num_local_key_value_heads, dims_per_head, dim)
+                for i in range(num_shards)
+            ],
+            dim=0,
+        ).reshape(key_value_dim, dim)
+
+        state_dict[f"model.layers.{layer_i}.self_attn.o_proj.weight"] = torch.cat(
+            [loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(num_shards)], dim=1
+        )
+        state_dict[f"model.layers.{layer_i}.mlp.gate_proj.weight"] = torch.cat(
+            [loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(num_shards)], dim=0
+        )
+        state_dict[f"model.layers.{layer_i}.mlp.down_proj.weight"] = torch.cat(
+            [loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(num_shards)], dim=1
+        )
+        state_dict[f"model.layers.{layer_i}.mlp.up_proj.weight"] = torch.cat(
+            [loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(num_shards)], dim=0
+        )
+
+        state_dict[f"model.layers.{layer_i}.self_attn.rotary_emb.inv_freq"] = inv_freq
+        for k, v in state_dict.items():
+            index_dict["weight_map"][k] = filename
+            param_count += v.numel()
+        torch.save(state_dict, os.path.join(tmp_model_path, filename))
+
+    filename = f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
+    state_dict = {
+        "model.norm.weight": loaded[0]["norm.weight"],
+        "model.embed_tokens.weight": torch.cat([loaded[i]["tok_embeddings.weight"] for i in range(num_shards)], dim=1),
+        "lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(num_shards)], dim=0),
+    }
+
+    for k, v in state_dict.items():
+        index_dict["weight_map"][k] = filename
+        param_count += v.numel()
+    torch.save(state_dict, os.path.join(tmp_model_path, filename))
+
+    # Write configs
+    index_dict["metadata"] = {"total_size": param_count * 2}
+    write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
+    config = MistralConfig(
+        hidden_size=dim,
+        intermediate_size=params["hidden_dim"],
+        num_attention_heads=params["n_heads"],
+        num_hidden_layers=params["n_layers"],
+        rms_norm_eps=params["norm_eps"],
+        num_key_value_heads=num_key_value_heads,
+        vocab_size=vocab_size,
+        rope_theta=base,
+        max_position_embeddings=max_position_embeddings,
+        sliding_window=sliding_window,
+    )
+    config.save_pretrained(tmp_model_path)
+
+    # Make space so we can load the model properly now.
+    del state_dict
+    del loaded
+    gc.collect()
+
+    print("Loading the checkpoint in a Mistral model.")
+    model = MistralForCausalLM.from_pretrained(tmp_model_path, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True)
+    # Avoid saving this as part of the config.
+    del model.config._name_or_path
+    model.config.torch_dtype = torch.float16
+    print("Saving in the Transformers format.")
+
+    model.save_pretrained(model_path, safe_serialization=safe_serialization)
+    shutil.rmtree(tmp_model_path)
+
+
+def write_tokenizer(tokenizer_path, input_tokenizer_path):
+    # Initialize the tokenizer based on the `spm` model
+    print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}.")
+    tokenizer = tokenizer_class(input_tokenizer_path)
+    tokenizer.save_pretrained(tokenizer_path)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--input_dir",
+        help="Location of Mistral weights, which contains tokenizer.model and model folders",
+    )
+    parser.add_argument(
+        "--model_size",
+        choices=["22B", "tokenizer_only"],
+        help="'f' models correspond to the finetuned versions, and are specific to the Mistral2 official release. For more details on Mistral2, checkout the original repo: https://huggingface.co/meta-mistral",
+    )
+    parser.add_argument(
+        "--output_dir",
+        help="Location to write HF model and tokenizer",
+    )
+    parser.add_argument("--safe_serialization", type=bool, help="Whether or not to save using `safetensors`.")
+    parser.add_argument(
+        "--is_v3", action="store_true", help="Whether the checkpoints correspond to the 3rd version or not."
+    )
+    args = parser.parse_args()
+    spm_path = os.path.join(args.input_dir, "tokenizer.model")
+    if args.model_size != "tokenizer_only":
+        write_model(
+            model_path=args.output_dir,
+            input_base_path=args.input_dir,
+            model_size=args.model_size,
+            safe_serialization=args.safe_serialization,
+            tokenizer_path=spm_path,
+            is_v3=args.is_v3,
+        )
+    else:
+        write_tokenizer(args.output_dir, spm_path)
+
+
+if __name__ == "__main__":
+    main()

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.40.2"
+}

model.safetensors.index.json

@@ -0,0 +1,514 @@
+{
+    "metadata": {
+        "total_size": 44494565376
+    },
+    "weight_map": {
+        "lm_head.weight": "model-00009-of-00009.safetensors",
+        "model.embed_tokens.weight": "model-00001-of-00009.safetensors",
+        "model.layers.0.input_layernorm.weight": "model-00001-of-00009.safetensors",
+        "model.layers.0.mlp.down_proj.weight": "model-00001-of-00009.safetensors",
+        "model.layers.0.mlp.gate_proj.weight": "model-00001-of-00009.safetensors",
+        "model.layers.0.mlp.up_proj.weight": "model-00001-of-00009.safetensors",
+        "model.layers.0.post_attention_layernorm.weight": "model-00001-of-00009.safetensors",
+        "model.layers.0.self_attn.k_proj.weight": "model-00001-of-00009.safetensors",
+        "model.layers.0.self_attn.o_proj.weight": "model-00001-of-00009.safetensors",
+        "model.layers.0.self_attn.q_proj.weight": "model-00001-of-00009.safetensors",
+        "model.layers.0.self_attn.v_proj.weight": "model-00001-of-00009.safetensors",
+        "model.layers.1.input_layernorm.weight": "model-00001-of-00009.safetensors",
+        "model.layers.1.mlp.down_proj.weight": "model-00001-of-00009.safetensors",
+        "model.layers.1.mlp.gate_proj.weight": "model-00001-of-00009.safetensors",
+        "model.layers.1.mlp.up_proj.weight": "model-00001-of-00009.safetensors",
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