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RWKV
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RWKV7-Goose-World3-2.9B-HF

Text Generation
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rwkv7
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  1. README.md +14 -11
  2. model-00001-of-00002.safetensors +3 -0
  3. model-00002-of-00002.safetensors +3 -0
  4. model.safetensors.index.json +1066 -0
README.md CHANGED
@@ -1,5 +1,6 @@
1
  ---
2
- license: apache-2.0
 
3
  language:
4
  - en
5
  - zh
@@ -9,14 +10,14 @@ language:
9
  - ar
10
  - es
11
  - pt
 
12
  metrics:
13
  - accuracy
14
- base_model:
15
- - BlinkDL/rwkv-7-world
16
  pipeline_tag: text-generation
17
  library_name: transformers
18
  ---
19
 
 
20
  # rwkv7-2.9B-world
21
 
22
  <!-- Provide a quick summary of what the model is/does. -->
@@ -44,15 +45,15 @@ This is RWKV-7 model under flash-linear attention format.
44
  <!-- Provide the basic links for the model. -->
45
 
46
  - **Repository:** https://github.com/fla-org/flash-linear-attention ; https://github.com/BlinkDL/RWKV-LM
47
- - **Paper:** [RWKV-7 "Goose" with Expressive Dynamic State Evolution](https://arxiv.org/abs/2503.14456)
48
 
49
  ## Uses
50
 
51
  <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
52
- Install `flash-linear-attention` <= 0.1.2 and the latest version of `transformers` before using this model:
53
 
54
  ```bash
55
- pip install --no-use-pep517 flash-linear-attention==0.1.2
56
  pip install 'transformers>=4.48.0'
57
  ```
58
 
@@ -64,11 +65,9 @@ You can use this model just as any other HuggingFace models:
64
  from transformers import AutoModelForCausalLM, AutoTokenizer
65
  model = AutoModelForCausalLM.from_pretrained('fla-hub/rwkv7-2.9B-world', trust_remote_code=True)
66
  tokenizer = AutoTokenizer.from_pretrained('fla-hub/rwkv7-2.9B-world', trust_remote_code=True)
67
- model = model.cuda()
68
  prompt = "What is a large language model?"
69
  messages = [
70
- {"role": "user", "content": "Who are you?"},
71
- {"role": "assistant", "content": "I am a GPT-3 based model."},
72
  {"role": "user", "content": prompt}
73
  ]
74
  text = tokenizer.apply_chat_template(
@@ -81,7 +80,11 @@ model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
81
 
82
  generated_ids = model.generate(
83
  **model_inputs,
84
- max_new_tokens=1024,
 
 
 
 
85
  )
86
  generated_ids = [
87
  output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
@@ -104,4 +107,4 @@ This model is trained on the World v3 with a total of 3.119 trillion tokens.
104
  ## FAQ
105
  Q: safetensors metadata is none.
106
 
107
- A: upgrade transformers to >=4.48.0: `pip install 'transformers>=4.48.0'`
 
1
  ---
2
+ base_model:
3
+ - BlinkDL/rwkv-7-world
4
  language:
5
  - en
6
  - zh
 
10
  - ar
11
  - es
12
  - pt
13
+ license: apache-2.0
14
  metrics:
15
  - accuracy
 
 
16
  pipeline_tag: text-generation
17
  library_name: transformers
18
  ---
19
 
20
+
21
  # rwkv7-2.9B-world
22
 
23
  <!-- Provide a quick summary of what the model is/does. -->
 
45
  <!-- Provide the basic links for the model. -->
46
 
47
  - **Repository:** https://github.com/fla-org/flash-linear-attention ; https://github.com/BlinkDL/RWKV-LM
48
+ - **Paper:** https://arxiv.org/abs/2503.14456
49
 
50
  ## Uses
51
 
52
  <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
53
+ Install `flash-linear-attention` and the latest version of `transformers` before using this model:
54
 
55
  ```bash
56
+ pip install git+https://github.com/fla-org/flash-linear-attention
57
  pip install 'transformers>=4.48.0'
58
  ```
59
 
 
65
  from transformers import AutoModelForCausalLM, AutoTokenizer
66
  model = AutoModelForCausalLM.from_pretrained('fla-hub/rwkv7-2.9B-world', trust_remote_code=True)
67
  tokenizer = AutoTokenizer.from_pretrained('fla-hub/rwkv7-2.9B-world', trust_remote_code=True)
68
+ model = model.cuda() # Supported on Nvidia/AMD/Intel eg. model.xpu()
69
  prompt = "What is a large language model?"
70
  messages = [
 
 
71
  {"role": "user", "content": prompt}
72
  ]
73
  text = tokenizer.apply_chat_template(
 
80
 
81
  generated_ids = model.generate(
82
  **model_inputs,
83
+ max_new_tokens=4096,
84
+ do_sample=True,
85
+ temperature=1.0,
86
+ top_p=0.3,
87
+ repetition_penalty=1.2
88
  )
89
  generated_ids = [
90
  output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
 
107
  ## FAQ
108
  Q: safetensors metadata is none.
109
 
110
+ A: upgrade transformers to >=4.48.0: `pip install 'transformers>=4.48.0'`
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