Update README.md

README.md

@@ -1,145 +1,30 @@
 ---
 pipeline_tag: text-generation
 license: other
-language:
-- en
-- zh
-tags:
-- math
 ---
-
-# InternLM-Math
+# InternLM 
 
 <div align="center">
 
-<img src="https://raw.githubusercontent.com/InternLM/InternLM/main/assets/logo.svg" width="200"/>
-  <div> </div>
+<img src="https://github.com/InternLM/InternLM/assets/22529082/b9788105-8892-4398-8b47-b513a292378e" width="200"/>
+  <div>&nbsp;</div>
   <div align="center">
-    <b><font size="5">InternLM-Math</font></b>
+    <b><font size="5">InternLM</font></b>
     <sup>
       <a href="https://internlm.intern-ai.org.cn/">
         <i><font size="4">HOT</font></i>
       </a>
     </sup>
-    <div> </div>
+    <div>&nbsp;</div>
   </div>
+  
+[![evaluation](https://github.com/InternLM/InternLM/assets/22529082/f80a2a58-5ddf-471a-8da4-32ab65c8fd3b)](https://github.com/internLM/OpenCompass/)
 
-State-of-the-art bilingual open-sourced Math reasoning LLMs.
-</div>
-
-# Introduction
-- **7B and 20B Chinese and English Math LMs with better than ChatGPT performances.** InternLM2-Math are continued pretrained from InternLM2-Base with ~100B high quality math-related tokens and SFT with ~2M bilingual math supervised data. We apply minhash and exact number match to decontaminate possible test set leakage. 
-- **Add Lean as a support language for math problem solving and math theorem proving.** We are exploring combining Lean 3 with InternLM-Math for verifiable math reasoning. InternLM-Math can generate Lean codes for simple math reasoning tasks like GSM8K or provide possible proof tactics based on Lean states.
-- **Also can be viewed as a reward model, which supports the Outcome/Process/Lean Reward Model.** We supervise InternLM2-Math with various types of reward modeling data, to make InternLM2-Math can also verify chain-of-thought processes. We also add the ability to convert a chain-of-thought process into Lean 3 code.
-- **A Math LM Augment Helper** and **Code Intepreter**. InternLM2-Math can help augment math reasoning problems and solve them using the code interpreter which makes you generate synthesis data quicker!
-
-# Models
-| Model | Transformers(HF) |Release Date |
-|---|---|---|
-| **InternLM2-Math-Base-7B**    | [🤗internlm/internlm2-math-base-7b](https://huggingface.co/internlm/internlm2-math-base-7b) | 2024-01-23|
-| **InternLM2-Math-Base-20B**    | [🤗internlm/internlm2-math-base-20b](https://huggingface.co/internlm/internlm2-math-base-20b) | 2024-01-23|
-| **InternLM2-Math-7B**    | [🤗internlm/internlm2-math-7b](https://huggingface.co/internlm/internlm2-math-7b) | 2024-01-23|
-| **InternLM2-Math-20B**    | [🤗internlm/internlm2-math-20b](https://huggingface.co/internlm/internlm2-math-20b) | 2024-01-23|
-
-
-# Performance
-
-## Pretrain Performance
-We evaluate pretrain checkpoints based on greedy decoding with few-shot COT. Details of pretraining will be introduced in the tech report.
-| Model                  |  GSM8K  | MATH   |
-|------------------------|---------|--------|
-| Llama2-7B |  11.8 | 3.2 |
-| Llemma-7B |  36.4 | 18.0 |
-| InternLM2-Base-7B |  36.5 | 8.6 |
-| **InternLM2-Math-Base-7B** |  **49.2** | **21.5** |
-| Minerva-8B |  16.2 | 14.1 |
-| InternLM2-Base-20B |  54.6 | 13.7 |
-| **InternLM2-Math-Base-20B** |  **63.7** | **27.3** |
-| Llemma-34B |  51.5 | 25.0 |
-| Minerva-62B |  52.4 | 27.6 |
-| Minerva-540B |  58.8 | 33.6 |
-
+[💻Github Repo](https://github.com/InternLM/InternLM)
 
-## SFT Peformance
-All performance is based on greedy decoding with COT. We notice that the performance of Hungary has a big variance between our different checkpoints, while other performance is very stable. This may be due to the problem amount about Hungary. 
-| Model                  | Model Type           | GSM8K  | MATH   | Hungary | 
-|------------------------|----------------------|--------|--------|---------|
-| Qwen-7B-Chat           | Genearl              | 51.7   | 11.6   | -       |
-| DeepSeek-7B-Chat       | General              | 63.0   | 15.8   | 28.5    | 
-| InternLM2-Chat-7B      | General              | 70.7   | 23.0   | -       |
-| ChatGLM3-6B            | General              | 53.8   | 20.4   | 32      |
-| MetaMath-Mistral-7B    | Mathematics          | 77.7   | 28.2   | 29      |
-| MetaMath-Llemma-7B     | Mathematics          | 69.2   | 30.0   | -       |
-| **InternLM2-Math-7B**      | Mathematics          | **78.1**   | **34.6**   | **55**      |
-| InternLM2-Chat-20B     | General              | 79.6   | 31.9   | -       | 
-| MetaMath-Llemma-34B    | Mathematics          | 75.8   | 34.8   | -       | 
-| **InternLM2-Math-20B**     | Mathematics          | **82.6**   | **37.7**   | **66**      | 
-| Qwen-72B               | General              | 78.9   | 35.2   | 52      | 
-| DeepSeek-67B           | General              | 84.1   | 32.6   | 58      | 
-| ChatGPT (GPT-3.5)      | General              | 80.8   | 34.1   | 41      |
-| GPT4 (First version)   | General              | 92.0   | 42.5   | 68      | 
-
-# Inference
-
-## LMDeploy
-We suggest using [LMDeploy](https://github.com/InternLM/LMDeploy)(>=0.2.1) for inference.
-```python
-from lmdeploy import pipeline, TurbomindEngineConfig, ChatTemplateConfig
-
-backend_config = TurbomindEngineConfig(model_name='internlm2-chat-7b', tp=1, cache_max_entry_count=0.3)
-chat_template = ChatTemplateConfig(model_name='internlm2-chat-7b', system='', eosys='', meta_instruction='')
-pipe = pipeline(model_path='internlm/internlm2-math-7b', chat_template_config=chat_template, backend_config=backend_config)
-
-problem = '1+1='
-result = pipe([problem], request_output_len=1024, top_k=1)
-```
-
-## Huggingface
-```python
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-math-7b", trust_remote_code=True)
-# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
-model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-math-7b", trust_remote_code=True, torch_dtype=torch.float16).cuda()
-model = model.eval()
-response, history = model.chat(tokenizer, "1+1=", history=[], meta_instruction="")
-print(response)
-```
-
-# Special usages
-We list some instructions used in our SFT. You can use them to help you. You can use the other ways to prompt the model, but the following are recommended. InternLM2-Math may combine the following abilities but it is not guaranteed.
-
-| Description | Query |
-| --- | --- |
-| Solving question via chain-of-thought | {Question} |
-| Solving question via Lean 3 | {Question}\nSolve this via Lean 3 |
-| Outcome reward model | Given a question and an answer, check is it correct?\nQuestion:{Question}\nAnswer:{COT} |
-| Process reward model | Given a question and an answer, check correctness of each step.\nQuestion:{Question}\nAnswer:{COT} |
-| Reward model | Given a question and two answers, which one is better? \nQuestion:{Question}\nAnswer 1:{COT}\nAnswer 2:{COT} |
-| Convert chain-of-thought to Lean 3 | Convert this answer into Lean3. Question:{Question}\nAnswer:{COT} |
-| Convert Lean 3 to chain-of-thought | Convert this lean 3 code into a natural language problem with answers:\n{LEAN} |
-| Translate question and chain-of-thought answer to a proof statement | Convert this question and answer into a proof format.\nQuestion:{Question}\nAnswer:{COT} |
-| Translate proof problem to Lean 3 | Convert this natural langauge statement into a Lean 3 theorem statement:{Theorem} |
-| Translate Lean 3 to proof problem | Convert this Lean 3 theorem statement into natural language:{STATEMENT} |
-| Suggest a tactic based on Lean state | Given the Lean 3 tactic state, suggest a next tactic:\n{State} |
-| Rephrase Problem | Describe this problem in another way. {STATEMENT} |
-| Augment Problem | Please augment a new problem based on: {Question} |
-| Augment a harder Problem | Increase the complexity of the problem: {Question} |
-| Change specific numbers | Change specific numbers: {Question}|
-| Introduce fractions or percentages | Introduce fractions or percentages: {Question}|
-| Code Intepreter | [lagent](https://github.com/InternLM/InternLM/blob/main/agent/lagent.md) |
-| In-context Learning | Question:{Question}\nAnswer:{COT}\n...Question:{Question}\nAnswer:{COT}|
+</div>
 
-# Fine-tune and others
-Please refer to [InternLM](https://github.com/InternLM/InternLM/tree/main).
 
-# Known issues
-Our model is still under development and will be upgraded. There are some possible issues of InternLM-Math.
-- Jump the calculating step.
-- Perform badly at Chinese fill-in-the-bank problems and English choice problems due to SFT data composition.
-- The reward model mode can be better leveraged with assigned token probabilities.
-- Code switch due to SFT data composition.
-- Some abilities of Lean can only be adapted to GSM8K-like problems (e.g. Convert chain-of-thought to Lean 3), and performance related to Lean is not guaranteed.
+## Converted using <a href="https://huggingface.co/chargoddard">Charles Goddard's</a> conversion script to create llama models from internlm
 
-# Citation and Tech Report
-To be appended.
+Original REPO link: https://huggingface.co/internlm/internlm2-math-20b