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README.md

@@ -1,5 +1,6 @@
 ---
-license: apache-2.0
+pipeline_tag: text-generation
+license: other
 ---
 # InternLM 
 
@@ -89,7 +90,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 model_dir = "internlm/internlm3-8b-instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_dir, 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(model_dir, trust_remote_code=True, torch_dtype=torch.float16)
+model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
 # (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes.
   # InternLM3 8B in 4bit will cost nearly 8GB GPU memory.
   # pip install -U bitsandbytes
@@ -104,7 +105,7 @@ messages = [
     {"role": "system", "content": system_prompt},
     {"role": "user", "content": "Please tell me five scenic spots in Shanghai"},
  ]
-tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
+tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
 
 generated_ids = model.generate(tokenized_chat, max_new_tokens=1024, temperature=1, repetition_penalty=1.005, top_k=40, top_p=0.8)
 
@@ -113,7 +114,7 @@ generated_ids = [
 ]
 prompt = tokenizer.batch_decode(tokenized_chat)[0]
 print(prompt)
-response = tokenizer.batch_decode(generated_ids)[0]
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 print(response)
 ```
 
@@ -160,7 +161,47 @@ Find more details in the [LMDeploy documentation](https://lmdeploy.readthedocs.i
 
 ####  Ollama inference
 
-TODO
+First install ollama,
+
+```python
+# install ollama
+curl -fsSL https://ollama.com/install.sh | sh
+# fetch model
+ollama pull internlm/internlm3-8b-instruct
+# install 
+pip install ollama
+```
+
+inference code,
+
+```python
+import ollama
+
+system_prompt = """You are an AI assistant whose name is InternLM (书生·浦语).
+- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.
+- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文."""
+
+messages = [
+    {
+        "role": "system",
+        "content": system_prompt,
+    },
+    {
+        "role": "user",
+        "content": "Please tell me five scenic spots in Shanghai"
+    },
+]
+
+stream = ollama.chat(
+    model='internlm/internlm3-8b-instruct',
+    messages=messages,
+    stream=True,
+)
+
+for chunk in stream:
+  print(chunk['message']['content'], end='', flush=True)
+```
+
 
 #### vLLM inference
 
@@ -168,7 +209,11 @@ We are still working on merging the PR(https://github.com/vllm-project/vllm/pull
 
 ```python
 git clone -b support-internlm3 https://github.com/RunningLeon/vllm.git
-pip install -e . 
+# and then follow  https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html#build-wheel-from-source to install 
+cd vllm
+python use_existing_torch.py
+pip install -r requirements-build.txt
+pip install -e . --no-build-isolatio
 ```
 
 inference code:
@@ -270,7 +315,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 model_dir = "internlm/internlm3-8b-instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_dir, 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(model_dir, trust_remote_code=True, torch_dtype=torch.float16)
+model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
 # (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes.
   # InternLM3 8B in 4bit will cost nearly 8GB GPU memory.
   # pip install -U bitsandbytes
@@ -282,7 +327,7 @@ messages = [
     {"role": "system", "content": thinking_system_prompt},
     {"role": "user", "content": "Given the function\(f(x)=\mathrm{e}^{x}-ax - a^{3}\),\n(1) When \(a = 1\), find the equation of the tangent line to the curve \(y = f(x)\) at the point \((1,f(1))\).\n(2) If \(f(x)\) has a local minimum and the minimum value is less than \(0\), determine the range of values for \(a\)."},
  ]
-tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
+tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
 
 generated_ids = model.generate(tokenized_chat, max_new_tokens=8192)
 
@@ -291,7 +336,7 @@ generated_ids = [
 ]
 prompt = tokenizer.batch_decode(tokenized_chat)[0]
 print(prompt)
-response = tokenizer.batch_decode(generated_ids)[0]
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 print(response)
 ```
 #### LMDeploy inference
@@ -321,14 +366,56 @@ print(response)
 
 ####  Ollama inference
 
-TODO
+First install ollama,
+
+```python
+# install ollama
+curl -fsSL https://ollama.com/install.sh | sh
+# fetch model
+ollama pull internlm/internlm3-8b-instruct
+# install
+pip install ollama
+```
+
+inference code,
+
+```python
+import ollama
+
+messages = [
+    {
+        "role": "system",
+        "content": thinking_system_prompt,
+    },
+    {
+        "role": "user",
+        "content": "Given the function\(f(x)=\mathrm{e}^{x}-ax - a^{3}\),\n(1) When \(a = 1\), find the equation of the tangent line to the curve \(y = f(x)\) at the point \((1,f(1))\).\n(2) If \(f(x)\) has a local minimum and the minimum value is less than \(0\), determine the range of values for \(a\)."
+    },
+]
+
+stream = ollama.chat(
+    model='internlm/internlm3-8b-instruct',
+    messages=messages,
+    stream=True,
+)
+
+for chunk in stream:
+  print(chunk['message']['content'], end='', flush=True)
+```
+
+
+#### 
 
 #### vLLM inference
 
 We are still working on merging the PR(https://github.com/vllm-project/vllm/pull/12037) into vLLM. In the meantime, please use the following PR link to install it manually.
 ```python
 git clone https://github.com/RunningLeon/vllm.git
-pip install -e .
+# and then follow  https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html#build-wheel-from-source to install 
+cd vllm
+python use_existing_torch.py
+pip install -r requirements-build.txt
+pip install -e . --no-build-isolatio
 ```
 
 inference code
@@ -438,7 +525,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 model_dir = "internlm/internlm3-8b-instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_dir, 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(model_dir, trust_remote_code=True, torch_dtype=torch.float16)
+model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
 # (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes.
   # InternLM3 8B in 4bit will cost nearly 8GB GPU memory.
   # pip install -U bitsandbytes
@@ -453,7 +540,7 @@ messages = [
     {"role": "system", "content": system_prompt},
     {"role": "user", "content": "Please tell me five scenic spots in Shanghai"},
  ]
-tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
+tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
 
 generated_ids = model.generate(tokenized_chat, max_new_tokens=1024, temperature=1, repetition_penalty=1.005, top_k=40, top_p=0.8)
 
@@ -462,7 +549,7 @@ generated_ids = [
 ]
 prompt = tokenizer.batch_decode(tokenized_chat)[0]
 print(prompt)
-response = tokenizer.batch_decode(generated_ids)[0]
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 print(response)
 ```
 
@@ -510,7 +597,49 @@ curl http://localhost:23333/v1/chat/completions \
 
 #####  Ollama 推理
 
-TODO
+准备工作
+
+```python
+# install ollama
+curl -fsSL https://ollama.com/install.sh | sh
+# fetch 模型
+ollama pull internlm/internlm3-8b-instruct
+# install python库
+pip install ollama
+```
+
+推理代码
+
+```python
+import ollama
+
+system_prompt = """You are an AI assistant whose name is InternLM (书生·浦语).
+- InternLM (书生·浦语) is a conversational language model that is developed by Shanghai AI Laboratory (上海人工智能实验室). It is designed to be helpful, honest, and harmless.
+- InternLM (书生·浦语) can understand and communicate fluently in the language chosen by the user such as English and 中文."""
+
+messages = [
+    {
+        "role": "system",
+        "content": system_prompt,
+    },
+    {
+        "role": "user",
+        "content": "Please tell me five scenic spots in Shanghai"
+    },
+]
+
+stream = ollama.chat(
+    model='internlm/internlm3-8b-instruct',
+    messages=messages,
+    stream=True,
+)
+
+for chunk in stream:
+  print(chunk['message']['content'], end='', flush=True)
+```
+
+
+#### 
 
 ##### vLLM 推理
 
@@ -518,7 +647,11 @@ TODO
 
 ```python
 git clone https://github.com/RunningLeon/vllm.git
-pip install -e .
+# and then follow  https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html#build-wheel-from-source to install 
+cd vllm
+python use_existing_torch.py
+pip install -r requirements-build.txt
+pip install -e . --no-build-isolatio
 ```
 
 推理代码
@@ -619,7 +752,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 model_dir = "internlm/internlm3-8b-instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_dir, 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(model_dir, trust_remote_code=True, torch_dtype=torch.float16)
+model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
 # (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes.
   # InternLM3 8B in 4bit will cost nearly 8GB GPU memory.
   # pip install -U bitsandbytes
@@ -631,7 +764,7 @@ messages = [
     {"role": "system", "content": thinking_system_prompt},
     {"role": "user", "content": "已知函数\(f(x)=\mathrm{e}^{x}-ax - a^{3}\)。\n（1）当\(a = 1\)时，求曲线\(y = f(x)\)在点\((1,f(1))\)处的切线方程；\n（2）若\(f(x)\)有极小值，且极小值小于\(0\)，求\(a\)的取值范围。"},
  ]
-tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
+tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
 
 generated_ids = model.generate(tokenized_chat, max_new_tokens=8192)
 
@@ -640,7 +773,7 @@ generated_ids = [
 ]
 prompt = tokenizer.batch_decode(tokenized_chat)[0]
 print(prompt)
-response = tokenizer.batch_decode(generated_ids)[0]
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 print(response)
 ```
 ##### LMDeploy 推理
@@ -670,7 +803,45 @@ print(response)
 
 #####  Ollama 推理
 
-TODO
+准备工作
+
+```python
+# install ollama
+curl -fsSL https://ollama.com/install.sh | sh
+# fetch 模型
+ollama pull internlm/internlm3-8b-instruct
+# install python库
+pip install ollama
+```
+
+inference code,
+
+```python
+import ollama
+
+messages = [
+    {
+        "role": "system",
+        "content": thinking_system_prompt,
+    },
+    {
+        "role": "user",
+        "content": "Given the function\(f(x)=\mathrm{e}^{x}-ax - a^{3}\),\n(1) When \(a = 1\), find the equation of the tangent line to the curve \(y = f(x)\) at the point \((1,f(1))\).\n(2) If \(f(x)\) has a local minimum and the minimum value is less than \(0\), determine the range of values for \(a\)."
+    },
+]
+
+stream = ollama.chat(
+    model='internlm/internlm3-8b-instruct',
+    messages=messages,
+    stream=True,
+)
+
+for chunk in stream:
+  print(chunk['message']['content'], end='', flush=True)
+```
+
+
+#### 
 
 ##### vLLM 推理
 
@@ -678,7 +849,11 @@ TODO
 
 ```python
 git clone https://github.com/RunningLeon/vllm.git
-pip install -e .
+# and then follow  https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html#build-wheel-from-source to install 
+cd vllm
+python use_existing_torch.py
+pip install -r requirements-build.txt
+pip install -e . --no-build-isolatio
 ```
 
 推理代码
@@ -728,4 +903,4 @@ print(outputs)
       archivePrefix={arXiv},
       primaryClass={cs.CL}
 }
-```
+```