Update README.md

README.md

@@ -1,203 +1,261 @@
 ---
+license: apache-2.0
 library_name: peft
+tags:
+- mistral
+datasets:
+- jondurbin/airoboros-2.2.1
+- Open-Orca/SlimOrca
+- garage-bAInd/Open-Platypus
+inference: false
+pipeline_tag: text-generation
 base_model: mistralai/Mixtral-8x7B-v0.1
 ---
 
-# Model Card for Model ID
+<div align="center">
 
-<!-- Provide a quick summary of what the model is/does. -->
+<img src="./logo.png" width="110px">
 
+</div>
 
 
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+# Mistral-7B-Instruct-v0.2
 
-[More Information Needed]
+A pretrained generative language model with 7 billion parameters geared towards instruction-following capabilities.
 
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+## Model Details
 
-## Environmental Impact
+This model was built via parameter-efficient finetuning of the [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) base model on the first 20k rows in each of the [jondurbin/airoboros-2.2.1](https://huggingface.co/datasets/jondurbin/airoboros-2.2.1), [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca), and [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus) datasets.
+
+- **Developed by:** Daniel Furman
+- **Model type:** Causal language model (clm)
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+
+## Model Sources 
+
+- **Repository:** [here](https://github.com/daniel-furman/sft-demos/blob/main/src/sft/mistral/sft_Mistral_7B_Instruct_v0_1_peft.ipynb)
+
+## Evaluation Results
+
+| Metric                | Value |
+|-----------------------|-------|
+| MMLU (5-shot)         | Coming |
+| ARC (25-shot)         | Coming |
+| HellaSwag (10-shot)   | Coming |
+| TruthfulQA (0-shot)   | Coming |
+| Avg.                  | Coming |
+
+We use Eleuther.AI's [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, the same version as Hugging Face's [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
+
+## Basic Usage
+
+<details>
+
+<summary>Setup</summary>
+
+```python
+!pip install -q -U transformers peft torch accelerate einops sentencepiece
+```
+
+```python
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+)
+```
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+```python
+peft_model_id = "dfurman/Mistral-7B-Instruct-v0.2"
+config = PeftConfig.from_pretrained(peft_model_id)
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+tokenizer = AutoTokenizer.from_pretrained(
+    peft_model_id,
+    use_fast=True,
+    trust_remote_code=True,
+)
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+model = AutoModelForCausalLM.from_pretrained(
+    config.base_model_name_or_path,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True,
+)
 
-## Technical Specifications [optional]
+model = PeftModel.from_pretrained(
+    model, 
+    peft_model_id
+)
+```
+
+</details>
 
-### Model Architecture and Objective
 
-[More Information Needed]
+```python
+messages = [
+    {"role": "user", "content": "Tell me a recipe for a mai tai."},
+]
 
-### Compute Infrastructure
+print("\n\n*** Prompt:")
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    return_tensors="pt",
+)
+print(tokenizer.decode(input_ids[0]))
 
-[More Information Needed]
+print("\n\n*** Generate:")
+with torch.autocast("cuda", dtype=torch.bfloat16):
+    output = model.generate(
+        input_ids=input_ids.cuda(),
+        max_new_tokens=1024,
+        do_sample=True,
+        temperature=0.7,
+        return_dict_in_generate=True,
+        eos_token_id=tokenizer.eos_token_id,
+        pad_token_id=tokenizer.pad_token_id,
+        repetition_penalty=1.2,
+        no_repeat_ngram_size=5,
+    )
 
-#### Hardware
+response = tokenizer.decode(
+    output["sequences"][0][len(input_ids[0]):], 
+    skip_special_tokens=True
+)
+print(response)
+```
 
-[More Information Needed]
+<details>
 
-#### Software
+<summary>Outputs</summary>
 
-[More Information Needed]
+**Prompt**:
+
+```python
+"<s> [INST] Tell me a recipe for a mai tai. [/INST]"
+```
+
+**Generation**:
+
+```python
+"""1. Combine the following ingredients in a cocktail shaker:
+2 oz light rum (or white rum)
+1 oz dark rum
+0.5 oz orange curacao or triple sec
+0.75 oz lime juice, freshly squeezed
+0.5 tbsp simple syrup (optional; if you like your drinks sweet)
+Few drops of bitters (Angostura is traditional but any will do)
+Ice cubes to fill the shaker
+
+2. Shake vigorously until well-chilled and combined.
+3. Strain into an ice-filled glass.
+4. Garnish with a slice of lime or an orange wedge, if desired."""
+```
+
+</details>
+
+
+## Speeds, Sizes, Times 
+
+| runtime / 50 tokens (sec) | GPU  | dtype | VRAM (GB) |
+|:-----------------------------:|:---------------------:|:-------------:|:-----------------------:|
+| 3.21                          | 1x A100 (40 GB SXM)                | torch.bfloat16    | 16                    |
+
+## Training
+
+It took ~5 hours to train 3 epochs on 1x A100 (40 GB SXM).
+
+### Prompt Format
+
+This model was finetuned with the following format:
+
+```python
+tokenizer.chat_template = "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST] ' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token + ' ' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}"
+```
+
+This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method. Here's an illustrative example:
 
-## Citation [optional]
+```python
+messages = [
+    {"role": "user", "content": "Tell me a recipe for a mai tai."},
+    {"role": "assistant", "content": "1 oz light rum\n½ oz dark rum\n¼ oz orange curaçao\n2 oz pineapple juice\n¾ oz lime juice\nDash of orgeat syrup (optional)\nSplash of grenadine (for garnish, optional)\nLime wheel and cherry garnishes (optional)\n\nShake all ingredients except the splash of grenadine in a cocktail shaker over ice. Strain into an old-fashioned glass filled with fresh ice cubes. Gently pour the splash of grenadine down the side of the glass so that it sinks to the bottom. Add garnishes as desired."},
+    {"role": "user", "content": "How can I make it more upscale and luxurious?"},
+]
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+print("\n\n*** Prompt:")
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    return_tensors="pt",
+)
+print(tokenizer.decode(input_ids[0]))
+```
 
-**BibTeX:**
+<details>
+
+<summary>Output</summary>
 
-[More Information Needed]
+```python
+"""<s> [INST] Tell me a recipe for a mai tai. [/INST] 1 oz light rum\n½ oz dark rum\n (...) Add garnishes as desired.</s>  [INST] How can I make it more upscale and luxurious? [/INST]"""
+```
+</details>
 
-**APA:**
+### Training Hyperparameters
 
-[More Information Needed]
 
-## Glossary [optional]
+We use the [SFTTrainer](https://huggingface.co/docs/trl/main/en/sft_trainer) from `trl` to fine-tune LLMs on instruction-following datasets.
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+See [here](https://github.com/daniel-furman/sft-demos/blob/main/src/sft/mistral/sft_Mistral_7B_Instruct_v0_1_peft.ipynb) for the finetuning code, which contains an exhaustive view of the hyperparameters employed.
 
-[More Information Needed]
+The following `TrainingArguments` config was used:
 
-## More Information [optional]
+- output_dir = "./results"
+- num_train_epochs = 2
+- auto_find_batch_size = True
+- gradient_accumulation_steps = 2
+- optim = "paged_adamw_32bit"
+- save_strategy = "epoch"
+- learning_rate = 3e-4
+- lr_scheduler_type = "cosine"
+- warmup_ratio = 0.03
+- logging_strategy = "steps"
+- logging_steps = 25
+- evaluation_strategy = "no"
+- bf16 = True
 
-[More Information Needed]
+The following `bitsandbytes` quantization config was used:
 
-## Model Card Authors [optional]
+- quant_method: bitsandbytes
+- load_in_8bit: False
+- load_in_4bit: True
+- llm_int8_threshold: 6.0
+- llm_int8_skip_modules: None
+- llm_int8_enable_fp32_cpu_offload: False
+- llm_int8_has_fp16_weight: False
+- bnb_4bit_quant_type: nf4
+- bnb_4bit_use_double_quant: False
+- bnb_4bit_compute_dtype: bfloat16
 
-[More Information Needed]
 
 ## Model Card Contact
 
-[More Information Needed]
+dryanfurman at gmail
+
+## Mistral Research Citation
 
+```
+@misc{jiang2023mistral,
+      title={Mistral 7B}, 
+      author={Albert Q. Jiang and Alexandre Sablayrolles and Arthur Mensch and Chris Bamford and Devendra Singh Chaplot and Diego de las Casas and Florian Bressand and Gianna Lengyel and Guillaume Lample and Lucile Saulnier and Lélio Renard Lavaud and Marie-Anne Lachaux and Pierre Stock and Teven Le Scao and Thibaut Lavril and Thomas Wang and Timothée Lacroix and William El Sayed},
+      year={2023},
+      eprint={2310.06825},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
 
 ### Framework versions