README.md

---
license: apache-2.0
tags:
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.3
model-index:
- name: home/migel/tess-2.5-mistral-7B-phase-1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false

load_in_8bit: false
load_in_4bit: false
strict: false
model_config:

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-multi_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml
  - path: /home/migel/ai_datasets/Tess-3.0/Tess-3.0-single_turn_chatml.jsonl
    type: sharegpt
    conversation: chatml

chat_template: chatml

dataset_prepared_path: last_run_prepared_mistral
val_set_size: 0.0
output_dir: /home/migel/tess-2.5-mistral-7B-phase-1

resume_from_checkpoint: /home/migel/tess-2.5-mistral-7B-phase-1/checkpoint-440 
auto_resume_from_checkpoints: true

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 4
num_epochs: 1
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: constant
learning_rate: 1e-6

wandb_project: mistral-7b
wandb_watch:
wandb_run_id:
wandb_log_model:

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 10
evals_per_epoch: 10
save_total_limit: 3
save_steps:
eval_sample_packing: false
debug:
deepspeed: /home/migel/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  pad_token: "<|end_of_text|>"


```

</details><br>

# Tess-3-7B-SFT

![Tess-3](https://huggingface.co/migtissera/Tess-v2.5-Qwen2-72B/resolve/main/Tess-v2.5.png)

Tess-3-7B is a finetuned version of the Mistral-7B-v0.3 base model. This version is the first phase of the final Tess-3 model, and have been trained with supervised fine-tuning (SFT) on a curated dataset of ~500K samples. The total SFT dataset contains about 1B tokens.

This model has 32K context length. 


# Sample code to run inference

Note that this model uses ChatML prompt format.

```python
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
from stop_word import StopWordCriteria

model_path = "migtissera/Tess-3-7B-SFT"
output_file_path = "/home/migel/conversations.jsonl"

model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.float16,
    device_map="auto",
    load_in_4bit=False,
    trust_remote_code=True,
)

tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

terminators = [
    tokenizer.convert_tokens_to_ids("<|im_end|>")
]

def generate_text(instruction):
    tokens = tokenizer.encode(instruction)
    tokens = torch.LongTensor(tokens).unsqueeze(0)
    tokens = tokens.to("cuda")

    instance = {
        "input_ids": tokens,
        "top_p": 1.0,
        "temperature": 0.75,
        "generate_len": 1024,
        "top_k": 50,
    }

    length = len(tokens[0])
    with torch.no_grad():
        rest = model.generate(
            input_ids=tokens,
            max_length=length + instance["generate_len"],
            use_cache=True,
            do_sample=True,
            top_p=instance["top_p"],
            temperature=instance["temperature"],
            top_k=instance["top_k"],
            num_return_sequences=1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=terminators,
        )
    output = rest[0][length:]
    string = tokenizer.decode(output, skip_special_tokens=True)
    return f"{string}"

conversation = f"""<|im_start|>system\nYou are Tesoro, a helful AI assitant. You always provide detailed answers without hesitation.<|im_end|>\n<|im_start|>user\n"""

while True:
    user_input = input("You: ")
    llm_prompt = f"{conversation}{user_input}<|im_end|>\n<|im_start|>assistant\n"
    answer = generate_text(llm_prompt)
    print(answer)
    conversation = f"{llm_prompt}{answer}\n"
    json_data = {"prompt": user_input, "answer": answer}

    with open(output_file_path, "a") as output_file:
        output_file.write(json.dumps(json_data) + "\n")
```

# Join My General AI Discord (NeuroLattice):
https://discord.gg/Hz6GrwGFKD

# Limitations & Biases:

While this model aims for accuracy, it can occasionally produce inaccurate or misleading results. 

Despite diligent efforts in refining the pretraining data, there remains a possibility for the generation of inappropriate, biased, or offensive content. 

Exercise caution and cross-check information when necessary. This is an uncensored model.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_migtissera__Tess-3-7B-SFT)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |17.10|
|IFEval (0-Shot)    |39.46|
|BBH (3-Shot)       |24.12|
|MATH Lvl 5 (4-Shot)| 3.32|
|GPQA (0-shot)      | 2.80|
|MuSR (0-shot)      |10.28|
|MMLU-PRO (5-shot)  |22.60|