Add files using upload-large-folder tool

README.md

@@ -1,214 +1,62 @@
 ---
-license: other
-license_name: deepseek-license
-license_link: LICENSE
+library_name: peft
+model_name: zenith-lora
+tags:
+- base_model:adapter:DeepSeek-Coder-V2-Lite-Instruct
+- lora
+- sft
+- transformers
+- trl
+licence: license
+base_model: DeepSeek-Coder-V2-Lite-Instruct
+pipeline_tag: text-generation
 ---
-<!-- markdownlint-disable first-line-h1 -->
-<!-- markdownlint-disable html -->
-<!-- markdownlint-disable no-duplicate-header -->
 
-<div align="center">
-  <img src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/logo.svg?raw=true" width="60%" alt="DeepSeek-V2" />
-</div>
-<hr>
-<div align="center" style="line-height: 1;">
-  <a href="https://www.deepseek.com/" target="_blank" style="margin: 2px;">
-    <img alt="Homepage" src="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/badge.svg?raw=true" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://chat.deepseek.com/" target="_blank" style="margin: 2px;">
-    <img alt="Chat" src="https://img.shields.io/badge/🤖%20Chat-DeepSeek%20V2-536af5?color=536af5&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://huggingface.co/deepseek-ai" target="_blank" style="margin: 2px;">
-    <img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-DeepSeek%20AI-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-</div>
+# Model Card for zenith-lora
 
-<div align="center" style="line-height: 1;">
-  <a href="https://discord.gg/Tc7c45Zzu5" target="_blank" style="margin: 2px;">
-    <img alt="Discord" src="https://img.shields.io/badge/Discord-DeepSeek%20AI-7289da?logo=discord&logoColor=white&color=7289da" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/figures/qr.jpeg?raw=true" target="_blank" style="margin: 2px;">
-    <img alt="Wechat" src="https://img.shields.io/badge/WeChat-DeepSeek%20AI-brightgreen?logo=wechat&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://twitter.com/deepseek_ai" target="_blank" style="margin: 2px;">
-    <img alt="Twitter Follow" src="https://img.shields.io/badge/Twitter-deepseek_ai-white?logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-</div>
+This model is a fine-tuned version of [None](https://huggingface.co/None).
+It has been trained using [TRL](https://github.com/huggingface/trl).
 
-<div align="center" style="line-height: 1;">
-  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-CODE" style="margin: 2px;">
-    <img alt="Code License" src="https://img.shields.io/badge/Code_License-MIT-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-  <a href="https://github.com/deepseek-ai/DeepSeek-V2/blob/main/LICENSE-MODEL" style="margin: 2px;">
-    <img alt="Model License" src="https://img.shields.io/badge/Model_License-Model_Agreement-f5de53?&color=f5de53" style="display: inline-block; vertical-align: middle;"/>
-  </a>
-</div>
-<p align="center">
-  <a href="#4-api-platform">API Platform</a> |
-  <a href="#5-how-to-run-locally">How to Use</a> |
-  <a href="#6-license">License</a> |
-</p>
+## Quick start
 
-
-<p align="center">
-  <a href="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/paper.pdf"><b>Paper Link</b>👁️</a>
-</p>
-
-# DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
-
-## 1. Introduction
-We present DeepSeek-Coder-V2, an open-source Mixture-of-Experts (MoE) code language model that achieves performance comparable to GPT4-Turbo in code-specific tasks. Specifically, DeepSeek-Coder-V2 is further pre-trained from an intermediate checkpoint of DeepSeek-V2 with additional 6 trillion tokens. Through this continued pre-training, DeepSeek-Coder-V2 substantially enhances the coding and mathematical reasoning capabilities of DeepSeek-V2, while maintaining comparable performance in general language tasks. Compared to DeepSeek-Coder-33B, DeepSeek-Coder-V2 demonstrates significant advancements in various aspects of code-related tasks, as well as reasoning and general capabilities. Additionally, DeepSeek-Coder-V2 expands its support for programming languages from 86 to 338, while extending the context length from 16K to 128K. 
-
-<p align="center">
-  <img width="100%" src="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/performance.png?raw=true">
-</p>
-
-
-In standard benchmark evaluations, DeepSeek-Coder-V2 achieves superior performance compared to closed-source models such as GPT4-Turbo, Claude 3 Opus, and Gemini 1.5 Pro in coding and math benchmarks.  The list of supported programming languages can be found [here](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/supported_langs.txt).
-
-## 2. Model Downloads
-
-We release the DeepSeek-Coder-V2 with 16B and 236B parameters based on the [DeepSeekMoE](https://arxiv.org/pdf/2401.06066) framework, which has actived parameters of only 2.4B and 21B , including base and instruct models, to the public. 
-
-<div align="center">
-
-|            **Model**            | **#Total Params** | **#Active Params** | **Context Length** |                         **Download**                         |
-| :-----------------------------: | :---------------: | :----------------: | :----------------: | :----------------------------------------------------------: |
-|   DeepSeek-Coder-V2-Lite-Base   |        16B        |        2.4B        |        128k        | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Base) |
-| DeepSeek-Coder-V2-Lite-Instruct |        16B        |        2.4B        |        128k        | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct) |
-|     DeepSeek-Coder-V2-Base      |       236B        |        21B         |        128k        | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Base) |
-|   DeepSeek-Coder-V2-Instruct    |       236B        |        21B         |        128k        | [🤗 HuggingFace](https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct) |
-
-</div>
-
-
-## 3. Chat Website
-
-You can chat with the DeepSeek-Coder-V2 on DeepSeek's official website: [coder.deepseek.com](https://coder.deepseek.com/sign_in)
-
-## 4. API Platform
-We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.com](https://platform.deepseek.com/), and you can also pay-as-you-go at an unbeatable price.
-<p align="center">
-  <img width="40%" src="https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/figures/model_price.jpg?raw=true">
-</p>
-
-
-## 5. How to run locally
-**Here, we provide some examples of how to use DeepSeek-Coder-V2-Lite model. If you want to utilize DeepSeek-Coder-V2 in BF16 format for inference, 80GB*8 GPUs are required.**
-
-### Inference with Huggingface's Transformers
-You can directly employ [Huggingface's Transformers](https://github.com/huggingface/transformers) for model inference.
-
-#### Code Completion
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
-input_text = "#write a quick sort algorithm"
-inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
-outputs = model.generate(**inputs, max_length=128)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
-
-#### Code Insertion
 ```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Base", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
-input_text = """<｜fim▁begin｜>def quick_sort(arr):
-    if len(arr) <= 1:
-        return arr
-    pivot = arr[0]
-    left = []
-    right = []
-<｜fim▁hole｜>
-        if arr[i] < pivot:
-            left.append(arr[i])
-        else:
-            right.append(arr[i])
-    return quick_sort(left) + [pivot] + quick_sort(right)<｜fim▁end｜>"""
-inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
-outputs = model.generate(**inputs, max_length=128)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):])
-```
-
-#### Chat Completion
-
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct", trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
-messages=[
-    { 'role': 'user', 'content': "write a quick sort algorithm in python."}
-]
-inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
-# tokenizer.eos_token_id is the id of <｜end▁of▁sentence｜>  token
-outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id)
-print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))
-```
-
-
-
-The complete chat template can be found within `tokenizer_config.json` located in the huggingface model repository.
-
-An example of chat template is as belows:
-
-```bash
-<｜begin▁of▁sentence｜>User: {user_message_1}
-
-Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2}
+from transformers import pipeline
 
-Assistant:
+question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
+generator = pipeline("text-generation", model="None", device="cuda")
+output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
 ```
 
-You can also add an optional system message:
+## Training procedure
 
-```bash
-<｜begin▁of▁sentence｜>{system_message}
+ 
 
-User: {user_message_1}
 
-Assistant: {assistant_message_1}<｜end▁of▁sentence｜>User: {user_message_2}
-
-Assistant:
-```
-
-### Inference with vLLM (recommended)
-To utilize [vLLM](https://github.com/vllm-project/vllm) for model inference, please merge this Pull Request into your vLLM codebase: https://github.com/vllm-project/vllm/pull/4650.
-
-```python
-from transformers import AutoTokenizer
-from vllm import LLM, SamplingParams
-
-max_model_len, tp_size = 8192, 1
-model_name = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True, enforce_eager=True)
-sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id])
-
-messages_list = [
-    [{"role": "user", "content": "Who are you?"}],
-    [{"role": "user", "content": "write a quick sort algorithm in python."}],
-    [{"role": "user", "content": "Write a piece of quicksort code in C++."}],
-]
-
-prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
-
-outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
-
-generated_text = [output.outputs[0].text for output in outputs]
-print(generated_text)
-```
+This model was trained with SFT.
 
+### Framework versions
 
+- PEFT 0.17.1
+- TRL: 0.24.0
+- Transformers: 4.56.1
+- Pytorch: 2.8.0+cu128
+- Datasets: 4.3.0
+- Tokenizers: 0.22.1
 
-## 6. License
+## Citations
 
-This code repository is licensed under [the MIT License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-CODE). The use of DeepSeek-Coder-V2 Base/Instruct models is subject to [the Model License](https://github.com/deepseek-ai/DeepSeek-Coder-V2/blob/main/LICENSE-MODEL). DeepSeek-Coder-V2 series (including Base and Instruct) supports commercial use.
 
 
-## 7. Contact
-If you have any questions, please raise an issue or contact us at [service@deepseek.com](service@deepseek.com).
+Cite TRL as:
+    
+```bibtex
+@misc{vonwerra2022trl,
+	title        = {{TRL: Transformer Reinforcement Learning}},
+	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
+	year         = 2020,
+	journal      = {GitHub repository},
+	publisher    = {GitHub},
+	howpublished = {\url{https://github.com/huggingface/trl}}
+}
+```

adapter_config.json

@@ -0,0 +1,37 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "DeepSeek-Coder-V2-Lite-Instruct",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7343364542f2d89df7ee70af0a716978469ef202dc3d6fac245ad13b62bb9e53
+size 4430800

chat_template.jinja

@@ -0,0 +1,5 @@
+{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '
+
+' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '
+
+' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}

checkpoint-250/README.md

@@ -0,0 +1,209 @@
+---
+base_model: DeepSeek-Coder-V2-Lite-Instruct
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:DeepSeek-Coder-V2-Lite-Instruct
+- lora
+- sft
+- transformers
+- trl
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## 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. -->
+
+[More Information Needed]
+
+### 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]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+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).
+
+- **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]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.17.1

checkpoint-250/adapter_config.json

@@ -0,0 +1,37 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "DeepSeek-Coder-V2-Lite-Instruct",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

checkpoint-250/adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d9dbbe1d159bae8d5888f55e7486b80b9bb5a13b892624d41549c7a544831bb9
+size 4430800

checkpoint-250/chat_template.jinja

@@ -0,0 +1,5 @@
+{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '
+
+' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '
+
+' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}

checkpoint-250/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ed2fa608eb8a22866330f11ead1cb01489a0f9a9f9f28601a033afd5c9b417ea
+size 8894283

checkpoint-250/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:71c5b73f36f731eae714b0c6e61953aa9bacdc1f34425a07a8b7044deb8edb4e
+size 14645

checkpoint-250/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18575b55e4c7b78b8a54f8e8c8b1c86e1bcd8797094ab4d47e178cd51b5e64a9
+size 1465

checkpoint-250/special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<｜begin▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoint-250/tokenizer_config.json

@@ -0,0 +1,162 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "100000": {
+      "content": "<｜begin▁of▁sentence｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100001": {
+      "content": "<｜end▁of▁sentence｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100002": {
+      "content": "<｜fim▁hole｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100003": {
+      "content": "<｜fim▁begin｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100004": {
+      "content": "<｜fim▁end｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100005": {
+      "content": "<｜completion｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100006": {
+      "content": "<｜User｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100007": {
+      "content": "<｜Assistant｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100008": {
+      "content": "<|EOT|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100009": {
+      "content": "<｜tool▁calls▁begin｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100010": {
+      "content": "<｜tool▁calls▁end｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100011": {
+      "content": "<｜tool▁call▁begin｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100012": {
+      "content": "<｜tool▁call▁end｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100013": {
+      "content": "<｜tool▁outputs▁begin｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100014": {
+      "content": "<｜tool▁outputs▁end｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100015": {
+      "content": "<｜tool▁output▁begin｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100016": {
+      "content": "<｜tool▁output▁end｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100017": {
+      "content": "<｜tool▁sep｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "bos_token": "<｜begin▁of▁sentence｜>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<｜end▁of▁sentence｜>",
+  "extra_special_tokens": {},
+  "legacy": true,
+  "model_max_length": 16384,
+  "pad_token": "<｜end▁of▁sentence｜>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizerFast",
+  "unk_token": null,
+  "use_default_system_prompt": false
+}

checkpoint-250/trainer_state.json

@@ -0,0 +1,284 @@
+{
+  "best_global_step": null,
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 83.33333333333333,
+  "eval_steps": 500,
+  "global_step": 250,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "entropy": 1.9060055136680603,
+      "epoch": 3.3333333333333335,
+      "grad_norm": 1.0164649486541748,
+      "learning_rate": 1.5e-05,
+      "loss": 4.1011,
+      "mean_token_accuracy": 0.4009168431162834,
+      "num_tokens": 5512.0,
+      "step": 10
+    },
+    {
+      "entropy": 1.9487085342407227,
+      "epoch": 6.666666666666667,
+      "grad_norm": 0.9628961682319641,
+      "learning_rate": 3.1666666666666666e-05,
+      "loss": 4.1923,
+      "mean_token_accuracy": 0.39442635476589205,
+      "num_tokens": 10326.0,
+      "step": 20
+    },
+    {
+      "entropy": 1.8993895709514619,
+      "epoch": 10.0,
+      "grad_norm": 1.177027702331543,
+      "learning_rate": 4.8333333333333334e-05,
+      "loss": 4.0334,
+      "mean_token_accuracy": 0.40877673774957657,
+      "num_tokens": 15462.0,
+      "step": 30
+    },
+    {
+      "entropy": 1.9950154840946197,
+      "epoch": 13.333333333333334,
+      "grad_norm": 1.436251163482666,
+      "learning_rate": 4.9863047384206835e-05,
+      "loss": 3.9342,
+      "mean_token_accuracy": 0.40931957215070724,
+      "num_tokens": 20681.0,
+      "step": 40
+    },
+    {
+      "entropy": 2.067208158969879,
+      "epoch": 16.666666666666668,
+      "grad_norm": 1.078131079673767,
+      "learning_rate": 4.939155700460536e-05,
+      "loss": 3.7698,
+      "mean_token_accuracy": 0.42098456621170044,
+      "num_tokens": 26109.0,
+      "step": 50
+    },
+    {
+      "entropy": 2.193948692083359,
+      "epoch": 20.0,
+      "grad_norm": 1.1934932470321655,
+      "learning_rate": 4.859021265983959e-05,
+      "loss": 3.6115,
+      "mean_token_accuracy": 0.43236732184886933,
+      "num_tokens": 31244.0,
+      "step": 60
+    },
+    {
+      "entropy": 2.4151065587997436,
+      "epoch": 23.333333333333332,
+      "grad_norm": 0.8595532774925232,
+      "learning_rate": 4.7469851157479177e-05,
+      "loss": 3.5401,
+      "mean_token_accuracy": 0.42654525190591813,
+      "num_tokens": 36738.0,
+      "step": 70
+    },
+    {
+      "entropy": 2.4695091366767885,
+      "epoch": 26.666666666666668,
+      "grad_norm": 0.9981549978256226,
+      "learning_rate": 4.604562346488144e-05,
+      "loss": 3.3173,
+      "mean_token_accuracy": 0.4566218167543411,
+      "num_tokens": 41886.0,
+      "step": 80
+    },
+    {
+      "entropy": 2.4938253283500673,
+      "epoch": 30.0,
+      "grad_norm": 0.5646995902061462,
+      "learning_rate": 4.43367898183491e-05,
+      "loss": 3.1822,
+      "mean_token_accuracy": 0.4696041285991669,
+      "num_tokens": 46983.0,
+      "step": 90
+    },
+    {
+      "entropy": 2.564500260353088,
+      "epoch": 33.333333333333336,
+      "grad_norm": 0.8602616786956787,
+      "learning_rate": 4.2366459261474933e-05,
+      "loss": 3.1409,
+      "mean_token_accuracy": 0.47821263819932935,
+      "num_tokens": 52150.0,
+      "step": 100
+    },
+    {
+      "entropy": 2.5386017203330993,
+      "epoch": 36.666666666666664,
+      "grad_norm": 0.7394922971725464,
+      "learning_rate": 4.0161277134970345e-05,
+      "loss": 2.9888,
+      "mean_token_accuracy": 0.49289675503969194,
+      "num_tokens": 57240.0,
+      "step": 110
+    },
+    {
+      "entropy": 2.5600393176078797,
+      "epoch": 40.0,
+      "grad_norm": 0.6606329083442688,
+      "learning_rate": 3.775106474414188e-05,
+      "loss": 2.9445,
+      "mean_token_accuracy": 0.5016584262251854,
+      "num_tokens": 62806.0,
+      "step": 120
+    },
+    {
+      "entropy": 2.547697913646698,
+      "epoch": 43.333333333333336,
+      "grad_norm": 0.8845142126083374,
+      "learning_rate": 3.516841607689501e-05,
+      "loss": 2.9015,
+      "mean_token_accuracy": 0.5097966089844703,
+      "num_tokens": 67877.0,
+      "step": 130
+    },
+    {
+      "entropy": 2.5584489583969114,
+      "epoch": 46.666666666666664,
+      "grad_norm": 0.8559042811393738,
+      "learning_rate": 3.244825702596205e-05,
+      "loss": 2.9204,
+      "mean_token_accuracy": 0.503237409889698,
+      "num_tokens": 73030.0,
+      "step": 140
+    },
+    {
+      "entropy": 2.479199802875519,
+      "epoch": 50.0,
+      "grad_norm": 0.9662401080131531,
+      "learning_rate": 2.9627373076117863e-05,
+      "loss": 2.7848,
+      "mean_token_accuracy": 0.5199230208992958,
+      "num_tokens": 78133.0,
+      "step": 150
+    },
+    {
+      "entropy": 2.451095724105835,
+      "epoch": 53.333333333333336,
+      "grad_norm": 0.7248209714889526,
+      "learning_rate": 2.674391184360313e-05,
+      "loss": 2.72,
+      "mean_token_accuracy": 0.530137126147747,
+      "num_tokens": 83587.0,
+      "step": 160
+    },
+    {
+      "entropy": 2.4100263833999636,
+      "epoch": 56.666666666666664,
+      "grad_norm": 0.8213902115821838,
+      "learning_rate": 2.3836867195056335e-05,
+      "loss": 2.6899,
+      "mean_token_accuracy": 0.5364036962389946,
+      "num_tokens": 88745.0,
+      "step": 170
+    },
+    {
+      "entropy": 2.3743161976337435,
+      "epoch": 60.0,
+      "grad_norm": 0.9216499924659729,
+      "learning_rate": 2.0945551922360818e-05,
+      "loss": 2.6164,
+      "mean_token_accuracy": 0.5464533418416977,
+      "num_tokens": 93971.0,
+      "step": 180
+    },
+    {
+      "entropy": 2.3309614181518556,
+      "epoch": 63.333333333333336,
+      "grad_norm": 0.9028099179267883,
+      "learning_rate": 1.8109066104575023e-05,
+      "loss": 2.577,
+      "mean_token_accuracy": 0.5494851708412171,
+      "num_tokens": 99176.0,
+      "step": 190
+    },
+    {
+      "entropy": 2.3660804867744445,
+      "epoch": 66.66666666666667,
+      "grad_norm": 1.1050912141799927,
+      "learning_rate": 1.5365768346438797e-05,
+      "loss": 2.6015,
+      "mean_token_accuracy": 0.5429237529635429,
+      "num_tokens": 104369.0,
+      "step": 200
+    },
+    {
+      "entropy": 2.2412913620471953,
+      "epoch": 70.0,
+      "grad_norm": 1.1248013973236084,
+      "learning_rate": 1.2752757044047827e-05,
+      "loss": 2.4471,
+      "mean_token_accuracy": 0.5652078241109848,
+      "num_tokens": 109867.0,
+      "step": 210
+    },
+    {
+      "entropy": 2.2687363147735597,
+      "epoch": 73.33333333333333,
+      "grad_norm": 0.8138008117675781,
+      "learning_rate": 1.0305368692688174e-05,
+      "loss": 2.4754,
+      "mean_token_accuracy": 0.5577909976243973,
+      "num_tokens": 115316.0,
+      "step": 220
+    },
+    {
+      "entropy": 2.2360614061355593,
+      "epoch": 76.66666666666667,
+      "grad_norm": 0.9668932557106018,
+      "learning_rate": 8.056700021356694e-06,
+      "loss": 2.4623,
+      "mean_token_accuracy": 0.5630308628082276,
+      "num_tokens": 120487.0,
+      "step": 230
+    },
+    {
+      "entropy": 2.28078556060791,
+      "epoch": 80.0,
+      "grad_norm": 0.9901016354560852,
+      "learning_rate": 6.037160416278278e-06,
+      "loss": 2.5002,
+      "mean_token_accuracy": 0.5567112416028976,
+      "num_tokens": 125629.0,
+      "step": 240
+    },
+    {
+      "entropy": 2.2194084882736207,
+      "epoch": 83.33333333333333,
+      "grad_norm": 1.047966480255127,
+      "learning_rate": 4.274060686123959e-06,
+      "loss": 2.418,
+      "mean_token_accuracy": 0.5643551826477051,
+      "num_tokens": 130811.0,
+      "step": 250
+    }
+  ],
+  "logging_steps": 10,
+  "max_steps": 300,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 100,
+  "save_steps": 50,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": false
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 1.5636363954499584e+16,
+  "train_batch_size": 2,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-250/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a0732842102da7bf8db229d843550c94a8d413ab0b06dfc753dfe4e84b41a97c
+size 6161

checkpoint-300/README.md

@@ -0,0 +1,209 @@
+---
+base_model: DeepSeek-Coder-V2-Lite-Instruct
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:DeepSeek-Coder-V2-Lite-Instruct
+- lora
+- sft
+- transformers
+- trl
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## 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. -->
+
+[More Information Needed]
+
+### 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]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+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).
+
+- **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]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.17.1

checkpoint-300/adapter_config.json

@@ -0,0 +1,37 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "DeepSeek-Coder-V2-Lite-Instruct",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

checkpoint-300/adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7343364542f2d89df7ee70af0a716978469ef202dc3d6fac245ad13b62bb9e53
+size 4430800

checkpoint-300/chat_template.jinja

@@ -0,0 +1,5 @@
+{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '
+
+' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '
+
+' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}

checkpoint-300/optimizer.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ae51a65c9ed8c154afedf866b6c74a2c1b4fab23491b2f85e65cc27c0354b507
+size 8894283

checkpoint-300/rng_state.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5b0db5f10bdf2f460ca79b7f667af15e47acf589fa2e9c0de2f69e533733a54e
+size 14645

checkpoint-300/scheduler.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6899f9f82cea064fe52ac09813b47d526ed686cbe04f0925eb7e665a0e61bfae
+size 1465

checkpoint-300/special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<｜begin▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

checkpoint-300/tokenizer_config.json

@@ -0,0 +1,162 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "100000": {
+      "content": "<｜begin▁of▁sentence｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100001": {
+      "content": "<｜end▁of▁sentence｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100002": {
+      "content": "<｜fim▁hole｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100003": {
+      "content": "<｜fim▁begin｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100004": {
+      "content": "<｜fim▁end｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100005": {
+      "content": "<｜completion｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100006": {
+      "content": "<｜User｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100007": {
+      "content": "<｜Assistant｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100008": {
+      "content": "<|EOT|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100009": {
+      "content": "<｜tool▁calls▁begin｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100010": {
+      "content": "<｜tool▁calls▁end｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100011": {
+      "content": "<｜tool▁call▁begin｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100012": {
+      "content": "<｜tool▁call▁end｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100013": {
+      "content": "<｜tool▁outputs▁begin｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100014": {
+      "content": "<｜tool▁outputs▁end｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100015": {
+      "content": "<｜tool▁output▁begin｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100016": {
+      "content": "<｜tool▁output▁end｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100017": {
+      "content": "<｜tool▁sep｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "bos_token": "<｜begin▁of▁sentence｜>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<｜end▁of▁sentence｜>",
+  "extra_special_tokens": {},
+  "legacy": true,
+  "model_max_length": 16384,
+  "pad_token": "<｜end▁of▁sentence｜>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizerFast",
+  "unk_token": null,
+  "use_default_system_prompt": false
+}

checkpoint-300/trainer_state.json

@@ -0,0 +1,334 @@
+{
+  "best_global_step": null,
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 100.0,
+  "eval_steps": 500,
+  "global_step": 300,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "entropy": 1.9060055136680603,
+      "epoch": 3.3333333333333335,
+      "grad_norm": 1.0164649486541748,
+      "learning_rate": 1.5e-05,
+      "loss": 4.1011,
+      "mean_token_accuracy": 0.4009168431162834,
+      "num_tokens": 5512.0,
+      "step": 10
+    },
+    {
+      "entropy": 1.9487085342407227,
+      "epoch": 6.666666666666667,
+      "grad_norm": 0.9628961682319641,
+      "learning_rate": 3.1666666666666666e-05,
+      "loss": 4.1923,
+      "mean_token_accuracy": 0.39442635476589205,
+      "num_tokens": 10326.0,
+      "step": 20
+    },
+    {
+      "entropy": 1.8993895709514619,
+      "epoch": 10.0,
+      "grad_norm": 1.177027702331543,
+      "learning_rate": 4.8333333333333334e-05,
+      "loss": 4.0334,
+      "mean_token_accuracy": 0.40877673774957657,
+      "num_tokens": 15462.0,
+      "step": 30
+    },
+    {
+      "entropy": 1.9950154840946197,
+      "epoch": 13.333333333333334,
+      "grad_norm": 1.436251163482666,
+      "learning_rate": 4.9863047384206835e-05,
+      "loss": 3.9342,
+      "mean_token_accuracy": 0.40931957215070724,
+      "num_tokens": 20681.0,
+      "step": 40
+    },
+    {
+      "entropy": 2.067208158969879,
+      "epoch": 16.666666666666668,
+      "grad_norm": 1.078131079673767,
+      "learning_rate": 4.939155700460536e-05,
+      "loss": 3.7698,
+      "mean_token_accuracy": 0.42098456621170044,
+      "num_tokens": 26109.0,
+      "step": 50
+    },
+    {
+      "entropy": 2.193948692083359,
+      "epoch": 20.0,
+      "grad_norm": 1.1934932470321655,
+      "learning_rate": 4.859021265983959e-05,
+      "loss": 3.6115,
+      "mean_token_accuracy": 0.43236732184886933,
+      "num_tokens": 31244.0,
+      "step": 60
+    },
+    {
+      "entropy": 2.4151065587997436,
+      "epoch": 23.333333333333332,
+      "grad_norm": 0.8595532774925232,
+      "learning_rate": 4.7469851157479177e-05,
+      "loss": 3.5401,
+      "mean_token_accuracy": 0.42654525190591813,
+      "num_tokens": 36738.0,
+      "step": 70
+    },
+    {
+      "entropy": 2.4695091366767885,
+      "epoch": 26.666666666666668,
+      "grad_norm": 0.9981549978256226,
+      "learning_rate": 4.604562346488144e-05,
+      "loss": 3.3173,
+      "mean_token_accuracy": 0.4566218167543411,
+      "num_tokens": 41886.0,
+      "step": 80
+    },
+    {
+      "entropy": 2.4938253283500673,
+      "epoch": 30.0,
+      "grad_norm": 0.5646995902061462,
+      "learning_rate": 4.43367898183491e-05,
+      "loss": 3.1822,
+      "mean_token_accuracy": 0.4696041285991669,
+      "num_tokens": 46983.0,
+      "step": 90
+    },
+    {
+      "entropy": 2.564500260353088,
+      "epoch": 33.333333333333336,
+      "grad_norm": 0.8602616786956787,
+      "learning_rate": 4.2366459261474933e-05,
+      "loss": 3.1409,
+      "mean_token_accuracy": 0.47821263819932935,
+      "num_tokens": 52150.0,
+      "step": 100
+    },
+    {
+      "entropy": 2.5386017203330993,
+      "epoch": 36.666666666666664,
+      "grad_norm": 0.7394922971725464,
+      "learning_rate": 4.0161277134970345e-05,
+      "loss": 2.9888,
+      "mean_token_accuracy": 0.49289675503969194,
+      "num_tokens": 57240.0,
+      "step": 110
+    },
+    {
+      "entropy": 2.5600393176078797,
+      "epoch": 40.0,
+      "grad_norm": 0.6606329083442688,
+      "learning_rate": 3.775106474414188e-05,
+      "loss": 2.9445,
+      "mean_token_accuracy": 0.5016584262251854,
+      "num_tokens": 62806.0,
+      "step": 120
+    },
+    {
+      "entropy": 2.547697913646698,
+      "epoch": 43.333333333333336,
+      "grad_norm": 0.8845142126083374,
+      "learning_rate": 3.516841607689501e-05,
+      "loss": 2.9015,
+      "mean_token_accuracy": 0.5097966089844703,
+      "num_tokens": 67877.0,
+      "step": 130
+    },
+    {
+      "entropy": 2.5584489583969114,
+      "epoch": 46.666666666666664,
+      "grad_norm": 0.8559042811393738,
+      "learning_rate": 3.244825702596205e-05,
+      "loss": 2.9204,
+      "mean_token_accuracy": 0.503237409889698,
+      "num_tokens": 73030.0,
+      "step": 140
+    },
+    {
+      "entropy": 2.479199802875519,
+      "epoch": 50.0,
+      "grad_norm": 0.9662401080131531,
+      "learning_rate": 2.9627373076117863e-05,
+      "loss": 2.7848,
+      "mean_token_accuracy": 0.5199230208992958,
+      "num_tokens": 78133.0,
+      "step": 150
+    },
+    {
+      "entropy": 2.451095724105835,
+      "epoch": 53.333333333333336,
+      "grad_norm": 0.7248209714889526,
+      "learning_rate": 2.674391184360313e-05,
+      "loss": 2.72,
+      "mean_token_accuracy": 0.530137126147747,
+      "num_tokens": 83587.0,
+      "step": 160
+    },
+    {
+      "entropy": 2.4100263833999636,
+      "epoch": 56.666666666666664,
+      "grad_norm": 0.8213902115821838,
+      "learning_rate": 2.3836867195056335e-05,
+      "loss": 2.6899,
+      "mean_token_accuracy": 0.5364036962389946,
+      "num_tokens": 88745.0,
+      "step": 170
+    },
+    {
+      "entropy": 2.3743161976337435,
+      "epoch": 60.0,
+      "grad_norm": 0.9216499924659729,
+      "learning_rate": 2.0945551922360818e-05,
+      "loss": 2.6164,
+      "mean_token_accuracy": 0.5464533418416977,
+      "num_tokens": 93971.0,
+      "step": 180
+    },
+    {
+      "entropy": 2.3309614181518556,
+      "epoch": 63.333333333333336,
+      "grad_norm": 0.9028099179267883,
+      "learning_rate": 1.8109066104575023e-05,
+      "loss": 2.577,
+      "mean_token_accuracy": 0.5494851708412171,
+      "num_tokens": 99176.0,
+      "step": 190
+    },
+    {
+      "entropy": 2.3660804867744445,
+      "epoch": 66.66666666666667,
+      "grad_norm": 1.1050912141799927,
+      "learning_rate": 1.5365768346438797e-05,
+      "loss": 2.6015,
+      "mean_token_accuracy": 0.5429237529635429,
+      "num_tokens": 104369.0,
+      "step": 200
+    },
+    {
+      "entropy": 2.2412913620471953,
+      "epoch": 70.0,
+      "grad_norm": 1.1248013973236084,
+      "learning_rate": 1.2752757044047827e-05,
+      "loss": 2.4471,
+      "mean_token_accuracy": 0.5652078241109848,
+      "num_tokens": 109867.0,
+      "step": 210
+    },
+    {
+      "entropy": 2.2687363147735597,
+      "epoch": 73.33333333333333,
+      "grad_norm": 0.8138008117675781,
+      "learning_rate": 1.0305368692688174e-05,
+      "loss": 2.4754,
+      "mean_token_accuracy": 0.5577909976243973,
+      "num_tokens": 115316.0,
+      "step": 220
+    },
+    {
+      "entropy": 2.2360614061355593,
+      "epoch": 76.66666666666667,
+      "grad_norm": 0.9668932557106018,
+      "learning_rate": 8.056700021356694e-06,
+      "loss": 2.4623,
+      "mean_token_accuracy": 0.5630308628082276,
+      "num_tokens": 120487.0,
+      "step": 230
+    },
+    {
+      "entropy": 2.28078556060791,
+      "epoch": 80.0,
+      "grad_norm": 0.9901016354560852,
+      "learning_rate": 6.037160416278278e-06,
+      "loss": 2.5002,
+      "mean_token_accuracy": 0.5567112416028976,
+      "num_tokens": 125629.0,
+      "step": 240
+    },
+    {
+      "entropy": 2.2194084882736207,
+      "epoch": 83.33333333333333,
+      "grad_norm": 1.047966480255127,
+      "learning_rate": 4.274060686123959e-06,
+      "loss": 2.418,
+      "mean_token_accuracy": 0.5643551826477051,
+      "num_tokens": 130811.0,
+      "step": 250
+    },
+    {
+      "entropy": 2.2408297181129457,
+      "epoch": 86.66666666666667,
+      "grad_norm": 1.1455246210098267,
+      "learning_rate": 2.7912437301749026e-06,
+      "loss": 2.4294,
+      "mean_token_accuracy": 0.5679019063711166,
+      "num_tokens": 135921.0,
+      "step": 260
+    },
+    {
+      "entropy": 2.2160379528999328,
+      "epoch": 90.0,
+      "grad_norm": 1.1439515352249146,
+      "learning_rate": 1.6087621040117157e-06,
+      "loss": 2.4361,
+      "mean_token_accuracy": 0.5633732363581657,
+      "num_tokens": 140722.0,
+      "step": 270
+    },
+    {
+      "entropy": 2.277903026342392,
+      "epoch": 93.33333333333333,
+      "grad_norm": 1.191327452659607,
+      "learning_rate": 7.426068431000882e-07,
+      "loss": 2.4947,
+      "mean_token_accuracy": 0.5539214983582497,
+      "num_tokens": 145887.0,
+      "step": 280
+    },
+    {
+      "entropy": 2.1880038678646088,
+      "epoch": 96.66666666666667,
+      "grad_norm": 1.1344318389892578,
+      "learning_rate": 2.0449121146845774e-07,
+      "loss": 2.3935,
+      "mean_token_accuracy": 0.5718189179897308,
+      "num_tokens": 150980.0,
+      "step": 290
+    },
+    {
+      "entropy": 2.2173574447631834,
+      "epoch": 100.0,
+      "grad_norm": 1.0592862367630005,
+      "learning_rate": 1.692299905944883e-09,
+      "loss": 2.4204,
+      "mean_token_accuracy": 0.5628041237592697,
+      "num_tokens": 156525.0,
+      "step": 300
+    }
+  ],
+  "logging_steps": 10,
+  "max_steps": 300,
+  "num_input_tokens_seen": 0,
+  "num_train_epochs": 100,
+  "save_steps": 50,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": true
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 1.8686345743478784e+16,
+  "train_batch_size": 2,
+  "trial_name": null,
+  "trial_params": null
+}

checkpoint-300/training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a0732842102da7bf8db229d843550c94a8d413ab0b06dfc753dfe4e84b41a97c
+size 6161

special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<｜begin▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<｜end▁of▁sentence｜>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -1,35 +1,162 @@
 {
   "add_bos_token": true,
   "add_eos_token": false,
-  "bos_token": {
-    "__type": "AddedToken",
-    "content": "<｜begin▁of▁sentence｜>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "100000": {
+      "content": "<｜begin▁of▁sentence｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100001": {
+      "content": "<｜end▁of▁sentence｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100002": {
+      "content": "<｜fim▁hole｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100003": {
+      "content": "<｜fim▁begin｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100004": {
+      "content": "<｜fim▁end｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100005": {
+      "content": "<｜completion｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100006": {
+      "content": "<｜User｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100007": {
+      "content": "<｜Assistant｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100008": {
+      "content": "<|EOT|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100009": {
+      "content": "<｜tool▁calls▁begin｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100010": {
+      "content": "<｜tool▁calls▁end｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100011": {
+      "content": "<｜tool▁call▁begin｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100012": {
+      "content": "<｜tool▁call▁end｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100013": {
+      "content": "<｜tool▁outputs▁begin｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100014": {
+      "content": "<｜tool▁outputs▁end｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100015": {
+      "content": "<｜tool▁output▁begin｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100016": {
+      "content": "<｜tool▁output▁end｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "100017": {
+      "content": "<｜tool▁sep｜>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
   },
+  "bos_token": "<｜begin▁of▁sentence｜>",
   "clean_up_tokenization_spaces": false,
-  "eos_token": {
-    "__type": "AddedToken",
-    "content": "<｜end▁of▁sentence｜>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  },
+  "eos_token": "<｜end▁of▁sentence｜>",
+  "extra_special_tokens": {},
   "legacy": true,
   "model_max_length": 16384,
-  "pad_token": {
-    "__type": "AddedToken",
-    "content": "<｜end▁of▁sentence｜>",
-    "lstrip": false,
-    "normalized": true,
-    "rstrip": false,
-    "single_word": false
-  },
+  "pad_token": "<｜end▁of▁sentence｜>",
   "sp_model_kwargs": {},
-  "unk_token": null,
   "tokenizer_class": "LlamaTokenizerFast",
-  "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{ 'User: ' + message['content'] + '\n\n' }}{% elif message['role'] == 'assistant' %}{{ 'Assistant: ' + message['content'] + eos_token }}{% elif message['role'] == 'system' %}{{ message['content'] + '\n\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}"
-}
+  "unk_token": null,
+  "use_default_system_prompt": false
+}