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Model: groxaxo/octen-embedding-8b-w4a16 Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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base_model: Octen/Octen-Embedding-8B
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library_name: transformers
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tags:
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- embeddings
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- quantized
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- w4a16
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- auto-round
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- auto-gptq
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---
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# Octen-Embedding-8B W4A16
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This repo contains a W4A16 quantized version of `Octen/Octen-Embedding-8B` in the validated `auto-round-auto-gptq` format.
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## Quantization
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| Item | Value |
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|---|---|
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| Base model | `Octen/Octen-Embedding-8B` |
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| Quantization | W4A16, 4-bit weights / 16-bit activations |
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| Tooling | AutoRound 0.12.2, transformers 5.6.2, torch 2.6.0+cu124 |
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| Calibration | 8 samples, seqlen 512, 200 iterations, float32 tuning |
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| Quantized size | 8.1 GB, 2 shards |
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| Base size | 15.0 GB |
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| Compression | ~1.9x |
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| Embedding dim | 4096 |
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| Layers quantized | 252/253; `lm_head` skipped |
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## Validation vs base model
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Evaluation used a small retrieval set of 5 query-document pairs, last-token pooling, L2 normalization, and cosine similarity.
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| Metric | Base | W4A16 | Delta |
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|---|---:|---:|---:|
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| Recall@1 | 0.8 | 1.0 | +0.2 |
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| Recall@5 | 1.0 | 1.0 | 0.0 |
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| Mean query cosine, base vs quant | — | 0.9840 | — |
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| Mean doc cosine, base vs quant | — | 0.9820 | — |
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Assessment: this model passed all validation gates cleanly, with >0.98 mean cosine to the base model and no retrieval degradation on the validation set.
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See `validation-8b-auto-round-auto-gptq.json` for the raw metrics.
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## RTX 3060 smoke test
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This quantized model was loaded and run on an RTX 3060 12GB GPU.
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| Result | Value |
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|---|---:|
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| VRAM after load | 4.53 GB |
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| Single short-query forward pass | 0.9s smoke test; later benchmark ~612ms |
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| Output shape | `[1, 4, 4096]` |
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| Embeddings | Valid normalized vectors; no NaNs observed |
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## Recommended usage
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```python
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import torch
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from transformers import AutoModel, AutoTokenizer
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model_id = "groxaxo/octen-embedding-8b-w4a16"
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModel.from_pretrained(
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model_id,
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trust_remote_code=True,
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torch_dtype=torch.float16,
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).cuda().eval()
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texts = ["how to implement binary search"]
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tokens = tokenizer(texts, padding=True, truncation=True, max_length=512, return_tensors="pt")
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tokens = {k: v.cuda() for k, v in tokens.items()}
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with torch.no_grad():
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out = model(**tokens)
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emb = torch.nn.functional.normalize(out.last_hidden_state[:, -1, :], p=2, dim=-1)
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```
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Note: the model card records local validation and smoke-test results. For production use, evaluate on your own retrieval distribution.
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "float32",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 12288,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 40960,
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"max_window_layers": 36,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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"pad_token_id": null,
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"quantization_config": {
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"amp": false,
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"autoround_version": "0.12.2",
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"batch_size": 1,
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"bits": 4,
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"gradient_accumulate_steps": 8,
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"group_size": 128,
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"low_gpu_mem_usage": true,
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"nsamples": 8,
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"packing_format": "auto_round:auto_gptq",
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"quant_method": "auto-round",
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"seqlen": 512,
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"sym": true
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},
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": false,
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"transformers_version": "5.6.2",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151665
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}
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15
quantization_config.json
Normal file
15
quantization_config.json
Normal file
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3
tokenizer.json
Normal file
3
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Normal file
@@ -0,0 +1,3 @@
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15
tokenizer_config.json
Normal file
15
tokenizer_config.json
Normal file
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15
validation-8b-auto-round-auto-gptq.json
Normal file
15
validation-8b-auto-round-auto-gptq.json
Normal file
@@ -0,0 +1,15 @@
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{
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}
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||||
Reference in New Issue
Block a user